{"paper_id":"52fd231f-4455-4dbd-9cab-1d0ff8799b25","body_text":"1\nTopographic alignment of auditory inputs to the visual cortex \n \nAlexander Egea-Weiss1,2,*, Benita Turner-Bridger 3,4,*, Aiste Viduolyte1,5, Elsa Marianelli1,6, Petr \nZnamenskiy3,§ and M. Florencia Iacaruso1,§   \n \n1 Neuronal Circuits and Behaviour Laboratory, The Francis Crick Institute, London, UK. \n2 Present address: Department of Biomedicine, University of Basel, Basel, Switzerland. \n3 Specification and Function of Neural Circuits Laboratory, The Francis Crick Institute, London, UK. \n4 Present address: VIB-KU Leuven Center for Brain & Disease Research, Leuven, Belgium. \n5 Present address: Neural Circuits and Immunity in Psychosis Laboratory, The Francis Crick Institute, \nLondon, UK. \n6 Present address: Human Electrophysiology Lab, UCL Department of Neuroscience, Physiology and \nPharmacology, London, UK \n* These authors contributed equally to this work \n§ These authors jointly supervised this work. \nCorresponding authors:  petr.znamenskiy@crick.ac.uk, florencia.iacaruso@crick.ac.uk  \n \n \nSensory cortical areas send long-range projections to cortical areas from other sensory modalities, supporting \nmultisensory integration to generate a unified representation of the external world. However, the organizational \nprinciples underlying these extensive cross-modal connections remain poorly understood. In this study, we \ninvestigated the anatomical and functional organisation of auditory cortex inputs in the visual cortex. We found \nthat populations of anatomically segregated auditory cortex neurons project to different visual cortical areas, \nbroadcasting distinct auditory information to the dorsal and ventral visual processing streams. While sound \nfrequency information was homogenously distributed across visual cortical areas, sound location information \nwas differentially broadcast across the visual cortex. Specifically, sound azimuth and elevation were \ndifferentially encoded across visual cortical areas and streams matching the retinotopic bias of the target area. \nThese findings suggest that cross-modal cortico-cortical connections follow a simple rule whereby specialised \nprojection pathways are topographically aligned with the organisational principles of the target sensory area, \nensuring spatially coherent integration of multisensory signals.  \n \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 2\nThe brain integrates information from multiple sensory modalities to form a coherent representation of the \nexternal world. This integration is supported by projections between cortical networks primarily subserving \ndifferent sensory modalities 1. The organizational principles of these cross-modal projections remain largely \nunknown. Sensory cortical areas are hierarchically organised into primary and higher-order areas 2–4. These \nhigher-order areas receive inputs from populations of neurons in primary sensory areas, which exhibit \nspecialised connectivity and functional properties 5–8. In the visual system, higher visual areas (HVAs) are \nfurther divided into two parallel processing pathways: the dorsal and ventral streams 9,10. In the mouse, visual \ncortical areas AL (anterolateral), RL(rostrolateral), A (anterior), AM (anteromedial), PM (posteromedial), have \nbeen proposed to constitute the dorsal visual stream, while areas P  (posterior), POR   (postrhinal), LI  \n(laterointermediate), LM (lateromedial) constitute the ventral stream 2,10–13. These streams are biased in their \nrepresentation of the visual field, particularly with respect to elevation: areas of the dorsal and ventral streams \nare biased toward the lower and upper visual fields, respectively. Areas within the same stream exhibit \ncorrelated activity during development 14,15, share feature selectivity 16,17, and have similar input-output \npatterns18–20. The two streams have been proposed to play distinct roles in vision, such as guiding perception \nversus action21,22 or exploration versus exploitation 23.  \nBoth primary and higher order areas of the visual cortex (VC) receive direct projections from the auditory cortex \n(AC)1,24–27. While spatial congruency is a hallmark of multisensory integration 28, the sound location encoded \nby AC axons does not align with the retinotopic representation of their local target population in the primary \nvisual cortex (V1) 29. Therefore, the logic of information transfer between the auditory cortex and the visual \ncortex remains to be deciphered. One possibility is that auditory information is uniformly broadcast across all \nvisual cortical areas (Fig. 1a, left) providing a complete representation of auditory features across both \nprocessing streams. Alternatively, auditory projection neurons may selectively target specific sets of visual \nareas (Fig. 1a, right), providing functional specificity to multisensory interactions. \nTo distinguish between these possibilities, we analyzed the anatomical and functional organisation of individual \nAC neurons projecting to the visual cortex. Using high-throughput sequencing of genetically barcoded neurons \n(MAPseq)30, we found that the organization of long-range axons of auditory cortex neurons projecting to the \nvisual cortex reflects the organization of dorsal and ventral processing streams. Specifically, AC neurons \nprojecting to the same stream have similar co-projection patterns, and often co-project to areas within the \nsame stream. Furthermore, we found that neurons projecting to the two streams are spatially segregated within \nauditory cortex. Using two-photon calcium imaging of axonal boutons from AC neurons in visual cortex, we \nfound that these specialised projection pathways convey distinct auditory spatial information across the visual \ncortex, with sound location tuning of auditory inputs to the HVAs aligned to the retinotopic location of the target \nvisual areas. These results reveal an unexpected precision of cross-modal projection pathways and suggest \na simple rule underlying cortical-cortical communication, whereby cross-modal information is differentially \nbroadcast according to the organising principles of the target area.  \nProjection patterns of single primary auditory cortical neurons to the visual cortex \nTo determine how information flow is patterned from the primary auditory cortex (A1) to the visual cortex and \nsurrounding cortical areas, we analysed the long-range axonal projections of thousands of neurons in parallel \nusing multiplexed analysis of projections by high throughput sequencing 30 (MAPseq). MAPseq relies on \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 3\nlabelling of neurons with random RNA barcodes that are transported to their axon terminals. Projection patterns \nof barcoded neurons can then be reconstructed by sequencing barcodes in dissected brain regions.  \nTo systematically characterize the projections of A1 neurons across visual cortex (VC) areas, we injected \nbarcoded Sindbis virus into A1 and used laser capture microdissection (LCM) to dissect the ipsilateral visual \ncortex and adjacent areas into ~400 x 600 µm “cubelets”. To distinguish between projections originating from \nsuperficial or deep layer neurons, we dissected the cubelets located within A1 into upper and lower layers. We \nalso collected LCM samples from other major projection targets of the auditory cortex, including the medial \ngeniculate nucleus in the thalamus, inferior and superior colliculus, contralateral AC, contralateral VC, and \nstriatum. We then registered the dissected cubelets to the Allen Common Coordinate Framework 31 and \nassigned neuron barcodes to different brain areas present in each cubelet 32 (Fig.1b).  \nWe analysed long-range projection patterns of 4,829 A1 neurons from three mice. To validate this approach, \nwe first compared bulk MAPseq barcode counts to the projection patterns derived from fluorescent \nanterograde tracing using data from the Allen Connectivity Atlas33 (Suppl. Fig. 2a-b). Total bulk MAPseq counts \nwere highly correlated with mean total projection density from three A1 fluorescent anterograde tracing \nexperiments at a cubelet level across the cortex (Pearson r = 0.65, p <10-42, Fig. 1c), similar to the correlations \nobserved between individual anterograde tracing experiments (mean ± S.D. = 0.75 ± 0.08; Suppl. Fig. 2c).  \nWe classified neurons into intratelencephalic (IT), pyramidal tract (PT), and corticothalamic (CT) cell types \nbased on their projections to the thalamus, superior and inferior colliculus. Neurons projecting to superior and \ninferior colliculus were labelled as PT, while the remaining neurons were classified as CT if they projected to \nthe thalamus or IT if they did not.  Corticostriatal projections in the auditory cortex originate from both IT and \nPT neurons 34. While IT-type corticostriatal neurons often send axon collaterals to the contralateral auditory \ncortex, PT-type neurons innervate the superior and inferior colliculus and the thalamus. Our data recapitulated \nthis segregation. Of 1,624 corticostriatal neurons, 689, 160, and 285 sent projections to the contralateral \ncortex, thalamus and inferior or superior colliculus, but only 3 projected both to the contralateral cortex and \nthalamus, and 2 projected to both contralateral cortex and inferior or superior colliculus (Fig. 1d).  Thus, \nreconstructed projection patterns of individual A1 neurons also followed expected cell-type specific cortical \nwiring rules. PT, CT, and IT neurons followed the expected layer distribution patterns, with PT and CT neurons \nfound predominantly in deep cortical layers and rarely observed in neurons originating from our superficial \nlayer A1 cubelets (21/885 upper layer neurons, Fig. 1e). We next used single neuron projection patterns across \nLCM cubelets to estimate their projection density across cortical areas. Since individual cubelets were small \n(mean 0.22 mm3), we assumed uniform barcode distribution within each cubelet and distributed the projection \nstrength between the areas contained within each cubelet according to their relative volumes (Suppl. Fig. 2d-\nf, see Methods). The estimated projection patterns at the area level matched those expected from bulk \nanterograde tracing data (Fig. 1f, Pearson’s r =0.85, p=7x10-8). Therefore, these data recapitulate the expected \nA1 connectivity patterns both at bulk and single neuron levels, confirming the specificity of MAPseq projection \ntracing.  \nWithin the cortex, A1 neurons showed diverse long-range projection patterns. While some targeted cortical \nareas in a spatially focal manner, others exhibited more distributed projections (Fig. 1g). The majority of A1 \nneurons targeted the auditory cortex and nearby areas such as the temporal association area (TEa) and most \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 4\n(>80%) also sent projections beyond auditory areas (Fig. 1h-i). Notably, A1 neurons targeting regions within \nthe visual cortex represented ~50% of corticocortical projecting neurons (Fig. 1i). \n \nFig. 1. High-throughput mapping of A1 single neuron projections.  a, Two hypothetical modes of information transfer from \nauditory cortex (AC) to the visual cortex. Auditory information (arrows) could be uniformly distributed across visual cortical areas \n(left) or selectively broadcast to specific visual areas (right). b, Experimental schematic for mapping single neuron projections \nusing MAPseq. The barcoded Sindbis virus library is injected into the primary auditory cortex (A1), labelling neurons with unique \nRNA barcodes (BC) that are transported into axon terminals. Brain regions of interest are dissected into cubelets via laser capture \nmicrodissection (LCM) and barcodes present in each cubelet are sequenced. c, Total bulk MAPseq counts in individual cortical \ntarget cubelets recapitulate conventional anterograde tracing projection patterns (Pearson correlation coefficient r = 0.65, p=7x10 -\n42; 338 cubelets from 3 mice, mean z-scored bulk GFP signal from 3 mice). d, MAPseq recapitulates brain-wide projection patterns \nof pyramidal tract (PT), corticothalamic (CT) and intratelencephalic (IT) neurons (4141 neurons across 3 mice).  e, Layer \ndistribution of IT, CT and PT neurons. CT and PT neurons are predominantly found in deep layer cubelets (885 and 1910 \nsuperficial and deep layer neurons).  f , Mean area-level corticocortical normalised projection density estimated by MAPseq \nrecapitulates conventional anterograde tracing (25 areas, N=3 mice for A1 MAPseq and N=3 mice for bulk anterograde tracing \nfrom A1; error bars are standard deviation across mice in log space back-transformed to linear scale; Pearson correlation \ncoefficient r=0.85, p=7x10 -8). g, Example projection patterns of single neurons across cortical cubelets visualized on a cortical \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 5\nflatmap (top) and their projection densities across cortical areas (bottom) normalized to the maximum for each neuron. Colormap \nindicates normalised projection density for the sampled areas. Areas in white were not sampled. h, Proportions of neurons \nprojecting only to majority auditory cortex cubelets and outside of the auditory cortex  ( N=3 mice). i, Proportions of neurons \ntargeting different cortical areas. Black bars group neurons projecting to any of the cortical areas specified on top of the panel. \nPurple bars show proportions for individual areas (N=3 mice).  \n \nA1 projections to dorsal and ventral visual streams form distinct subnetworks \nWe next focused on the projection patterns of single A1 neurons in the visual cortex. VC-projecting A1 neurons \nmost frequently targeted the POR, AL, and LI (Fig. 1i), which are closest to the A1 injection sites (Fig. 2a). \nIndeed, exponential decay of connection probability with distance is thought to serve as a general cortical \nwiring principle35,36. Consistent with this, we found that the frequency and normalised density of projections to \ncortical areas including visual areas were well fit by exponential decay with distance from A1 (p=4.3x10 -6 and \n9x10-5, respectively, Fig. 2b–c).  \nBecause A1 is tonotopically organised along the anterior-posterior (A-P) axis 37–39, we asked whether the \nprojection frequency to specific VC areas depends on the A-P soma location of A1 neurons. For individual VC \ncubelets, their A-P position was positively correlated with the mean A-P position of somata of neurons \ninnervating them (Fig. 2d-e; Pearson correlation coefficient = 0.49, p<1x10 -6). On average, anterior VC areas \nreceived input from neurons located more anterior in A1 (Fig. 2f). At the single neuron level, the probability of \ntargeting anterior visual areas A and RL was positively associated with A-P position of the neurons’ somata \n(Fig. 2g). Conversely, posterior visual areas POR and P were preferentially targeted by neurons in posterior \nA1 and the probability of targeting them was inversely related to soma position (Fig. 2g and Suppl. Fig. 4a). \nThese data indicate that projections from A1 to the VC are topographically organised along the A-P axis. \nImportantly, this organisation cannot be explained by physical proximity alone since we observed a significant \nrelationship between A-P soma cubelet position and VC areas AM, RL, P and POR after controlling for soma \ncubelet-to-VC area distance (Fig. 2g; p=6.7x10 -4, 2.6x10-26, 5.6x10-41, and 3.1x10 -8 for AM, RL, POR and P, \nrespectively; logistic regression with mouse and distance as covariates). This trend was further confirmed from \nbulk anterograde tracing experiments obtained from the Allen Connectivity Atlas. We observed that axonal \nEGFP expression from anterior A1 injections was higher in anterior visual areas such as RL and A, while \nposterior A1 neurons targeted ventral areas with greater strength (Suppl. Fig. 4b-d). Together, these findings \nindicate that relative position along the A-P axis rather than sheer proximity alone best explains the observed \ntopographic wiring of A1 projections across VC. The anterior VC areas AL, RL, A, AM, PM, have been \nproposed to constitute the dorsal visual stream, while posterior VC areas P, POR, LI, LM constitute the ventral \nstream2,10. This suggests that neurons located in anterior A1 make specialised projections to dorsal visual \nareas, while those in posterior A1 target the ventral stream.  \nWe next asked whether individual A1 neurons establish dedicated projections to individual VC areas or target \nmultiple VC areas simultaneously. We found that fewer than half (38%, 1,018/2,647) of VC-projecting A1 \nneurons innervated a single VC area, while the majority projected to multiple targets (Fig. 2h). This shows that \nsimilar to V1 projections40 individual A1 neurons broadcast information across multiple VC areas. To quantify \nthe co-projection patterns of VC-projecting A1 neurons, we calculated the conditional probability P(target|VC \narea) of A1 neurons targeting different cortical regions given projections to a particular VC area (Fig. 2i). We \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 6\ncompared these conditional probabilities to shuffled connectivity matrices that preserved the statistical \nstructure of the dataset at the single area and single neuron level, including marginal projection probabilities, \nintrinsic biases in assignment of projections emerging from cubelet structure, and variability in barcode \nlabelling efficiencies (Fig. 2j, Suppl. Fig. 3, see Methods). \nWe observed A1 neurons targeting dorsal stream areas AM, A, PM, and RL had a higher probability of co-\nprojecting to other dorsal stream VC areas compared to the shuffled population (Fig. 2j). In addition, A1 \nneurons projecting to dorsal stream areas AM, A, PM were more likely to co-project to somatosensory, motor \nand anterior cingulate cortical areas (Fig. 2i). These projection biases were not observed for neurons \ninnervating the ventral visual stream areas LM, LI, POR and P. Neurons projecting to both dorsal and ventral \nstream visual areas were less likely to co-project to the contralateral cortex and ventral auditory area (AUDv) \ncompared to the shuffle control. Finally, neurons projecting to area AL were less likely to co-project to other \nvisual, retrosplenial and motor cortical areas. \nTo quantify the similarity in projection patterns of A1 neurons targeting different VC areas, we computed the \ncosine similarity between their conditional probability vectors (Fig. 2k). A1 neurons targeting ventral stream \nVC areas exhibited co-projection patterns that were more similar to those of A1 neurons targeting other ventral \nstream VC areas than to those projecting to dorsal stream VC regions. The converse pattern was observed \nfor A1 neurons projecting to dorsal stream VC areas. Interestingly, we observed projection patterns for A1 \nneurons that targeted AL had higher similarity to A1 co-projections involving ventral stream VC areas. This \nfinding is consistent with recent retrograde tracing studies 41, which demonstrated that brain-wide input \nconnectivity of AL neurons more closely resembles that of ventral stream areas, even though AL retains \nfeatures of both streams. Because of this transitional status and higher similarity with ventral stream regions \nin the context of single A1 neuron co-projection patterns in our dataset, we classed AL as belonging to the \nventral stream in our analyses. These data demonstrate that A1 neurons projecting to the dorsal and ventral \nstreams are organised into distinct subnetworks with heightened similarity in co-projection patterns within \nstreams. \nTo further probe the organisational logic of co-projection patterns of A1→VC connections, we compared the \nfrequency of co-innervation between pairs of cortical areas to that expected by chance given the null \nhypothesis that neurons project to each target region independently. This analysis identified both under- and \nover-represented A1 bifurcation motifs (Fig. 2k). For example, co-projections to VC areas and the ectorhinal \ncortex (ECT) and the ventral auditory area (AUDv) were under-represented compared to the shuffle control. \nOn the other hand, co-projections targeting multiple VC areas were over-represented. Moreover, we found \nthat within stream co-projections between pairs of dorsal stream areas tended to have greater over-\nrepresentation compared to across stream or ventral-ventral co-projection motifs (Fig. 2l,m, p=0.0015; Mann-\nWhitney U test).  \nTogether these analyses suggests that projections from A1 to the visual cortex are organised into specialized \npathways, often co-projecting to multiple targets and broadcasting auditory information to specific \ncombinations of visual areas. Neurons in posterior A1 preferentially target posterior HVAs belonging to the \nventral visual stream. On the other hand, neurons in anterior A1 preferentially project to anterior dorsal stream \nareas and send collaterals to somatosensory, motor and anterior cingulate cortices. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 7\n \nFigure 2. Topography of single A1 neuron projections across visual cortical areas.  a, Cortical flatmap showing mean \nnormalised neuron projection densities, cropped to visualise VC areas (N=3 mice). Dots indicate source site centroids. b-c, \nTargeting frequency (c), and normalised projection density for neurons projecting to ipsilateral cortical areas decay exponentially \nwith distance. Dotted line shows exponential fit. d, Cortical flatmap showing mean A1 soma position for neuron projections to \ncubelets containing VC areas. Dots indicate centroid positions for A1 source site cubelets coloured according to A-P position. To \nminimize bias from local A1 projections and from voxels with too few neuronal projections, we analysed only cubelets containing \n<10% A1 and voxels containing > 3 projections. e, A-P position for cubelets in the visual cortex plotted against mean A-P source \nsite position for neurons targeting that VC cubelet. All values are expressed relative to the most posterior VC position. f, Mean \nnormalised soma A-P position for A1 neurons targeting different VC areas. Individual points are mean for each mouse. VC areas \nare ordered in posterior to anterior positions  g, Relationship between A-P source site position and targeting frequency. Y-axis is \nfraction of neurons targeting each plotted visual area for neurons in a particular A-P source site position. X-axis is binned A1 \nsource site A-P position with scale in microns, values are relative to most posterior VC position. Logistic regression analysis \ncontrolling for inter-mouse variability and area-to-soma cubelet distance and adjusting for multiple comparisons (Bonferroni) \nshowed significant effects for AM (p=6.7x10 -4), RL (p=2.6x10 -26), POR (p=5.6x10 -41), and P (p=3.1x10 -8). For visualisation, fitted \nlines are logistic regression model predictions for all mice together for each A-P bin position bin using the average Euclidean \ndistance of neurons in that bin as a covariate. Shaded bands are 95% confidence interval. h, Histogram showing number of \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 8\ndifferent VC targets of individual VC-projecting A1 neurons (N=2,647 neurons). i, Heatmap of conditional probability for A1 \nneurons targeting different cortical regions given they already project to a specific target VC area. j, Bubble plot corresponding to \nconditional probabilities shown in i, with circle colours indicating increased (red) or decreased (blue) conditional probabilities \ncompared to mean shuffled population. Colorbar indicates strength of p-value, bubble size indicates effect size and black outline \nindicates p<0.05 for two-tailed p-values compared to the null population of 100,000 shuffles and after Bonferroni correction. p-\nvalues are capped at 10 -5. k, Mean cosine similarities across 3 mice for conditional probabilities across the ipsilateral cortex for \nA1 neurons targeting different VC areas. l, Volcano plot showing statistical significance of co-projection motifs involving VC areas \nand related log fold change over mean shuffled effect size. Here, effect size represents the number of observed co-projections \nbetween two targets in our data divided by the expected number of observing co-projection by chance. The significance values \non the y-axis are capped at 50. m, Relative effect size compared to shuffled null population for representation of co-projection \nmotifs (as in panel l) grouped according to streams (p=0.0015, Mann-Whitney test). \n \nSound frequency is transmitted uniformly to visual cortex \nThe anatomical segregation of A1→VC projections suggests that auditory signals might be transmitted \ndifferently to the two visual processing streams. As auditory cortex is tonotopically organised and neurons \nprojecting to ventral and dorsal stream visual areas show different biases in their spatial location within A1 \n(Fig. 2d,e), we hypothesised that A1 neurons projecting to the two streams differ in their tuning to sound \nfrequency (Fig. 3a). To test this hypothesis, we expressed the calcium indicator jGCaMP7b in auditory cortex \nneurons (Fig. 3b and Suppl. Fig.5a-c) and imaged their axonal projections in layer 1 of V1 and HVAs using \ntwo-photon microscopy.  \njGCaMP7b expression location within auditory cortex (AC) varied across animals and was not always confined \nto A1 (Suppl. Fig. 5a-c). Therefore, we hereafter refer to the population we recorded from as “AC inputs” and \naccounted for injection location in the analysis of functional responses by including each animal’s injection \nlocation as a factor in mixed-effects models. We first identified the location of V1 and HVAs using intrinsic \nsignal imaging (Fig. 3b-c and Suppl. Fig. 5d). We then measured the responses of axonal boutons of AC \nprojection neurons in these areas to pure tones of different frequencies (2-64 kHz) and sound intensities (Fig. \n3d-e, 40-, 50- and 60-dB SPL). AC→VC boutons were tuned to a range of frequencies and sound intensities, \nand exhibited both single peaked and multi-peaked frequency response areas 42 (FRAs, Fig. 3e). Moreover, \nwhile sound presentation increased mouse facial motion (Suppl. Fig. 6), sound frequency tuning of AC→VC \nboutons was largely unaffected by controlling for motor-related signals using a linear regression model (Suppl. \nFig. 7). In all subsequent analyses, motor-related signals were removed from calcium traces before analysing \ntheir sound response properties. Overall, 39% of AC→VC boutons exhibited robust frequency selectivity, and \nthe proportion of frequency-tuned boutons was consistent across all cortical visual areas (Fig 3e-f, p = 0.2, \nmixed effects ANOVA).  \nWe next examined the representation of sound frequency across visual processing streams. If the \nrepresentation of sound frequency by AC→VC projections segregates in a stream-like manner, auditory inputs \nto areas within the same stream should have more similar tone frequency tuning than inputs to areas in \ndifferent streams. To test this, we assessed tuning similarity between tone-responsive boutons by computing \nthe pairwise signal correlation of their frequency response areas (FRAs). We then averaged the signal \ncorrelations between boutons of different cortical visual areas and streams. Contrary to our predictions, the \naverage signal correlations between areas of the same stream were not higher than those between areas of \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 9\ndifferent streams (Fig. 3g-h, ventral-ventral vs dorsal-ventral, p = 0.49; dorsal-dorsal vs dorsal-ventral, p = \n0.72). Indeed, the median preferred frequency did not differ between AC inputs to the two streams or across \nareas (Fig. 3i-j, dorsal vs ventral: p = 0.35, across areas: p = 0.26, mixed effects ANOVA). A closer comparison \nof the tuning properties of AC inputs to the different VC areas did not reveal differences in their representation \nof pure tones, including best frequency, frequency selectivity, or presence of multi-peaked FRAs, except for a \nbroadening of the frequency tuning curves for AC→V1 compared to AC→HVAs boutons (Fig. 3j and Suppl. \nFig. 8a-h). Moreover, best tone frequency did not correlate with the anatomical position of the auditory cortex \ninjection site, suggesting that tonotopy is not reflected in AC→VC projections (Suppl. Fig. 8i-j). This is \nconsistent with the observation that although the mouse auditory cortex is tonotopically organised at the \nmacroscopic scale, sound frequency tuning is heterogeneous at the fine scale, showing high variability within \nlocal (~400µm) populations43. Thus, the anatomical segregation of auditory inputs to the two visual streams is \nnot reflected in their sound frequency tuning. Instead, sound frequency information is broadly distributed \nacross visual areas. \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 10 \n \n \nFigure 3. Encoding of sound location, but not frequency, differs across AC projections to visual streams. \na, Schematic of potential tonotopic organization of auditory projections to visual streams. b, Schematic of experimental \nstrategy. The calcium indicator jGCaMP7b was expressed in auditory cortex, and axonal projections were imaged in the \nvisual cortex. c, Example of chronic imaging window implanted above visual cortex (left), overlaid with retinotopic map \nobtained with intrinsic imaging and visual area boundaries (black lines). Green square indicates location of example field-\nof-view (right), dashed squares indicate other recording sites from the same mouse. d, Schematic of auditory stimulation \nstrategy for frequency-tuning characterisation. Head-fixed mice were presented with pure tones of different frequencies \nand intensities (Methods). e, Example fluorescence of one example AC bouton aligned to the onset of pure tone stimuli \n(left, top) and frequency response areas (FRAs) of same bouton (left bottom) and other example boutons (right). \nf, Percentage of tone-responsive boutons in each recording that were frequency-selective (ANOVA, p < 0.05), grouped by \nvisual area. 1 circle = 1 recording, horizontal bar = median across recordings. Gray p-value from mixed effects ANOVA \nwith visual area as fixed effect (Methods). Ntotal = 442648 boutons, Nresponsive = 96539 boutons, Nfreq.selective = 33522 boutons, \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 11 \n15 animals. g, Matrix of average pairwise signal correlations in pure-tone responses between AC boutons grouped by \nvisual area. h, Signal correlation in pure-tone responses between boutons of the same or different streams. Black lines \nindicate hierarchically bootstrapped 95% confidence interval . Significance was assessed from the hierarchically \nbootstrapped difference in signal correlations (Methods).  i, Median best frequency of boutons in each recording, grouped \nby stream. P-value obtained via mixed effects ANOVA with injection location and animal as fixed and random effects \nrespectively. j, As in i, with recordings grouped by visual area. k, Schematic of potential difference in encoding of sound \nlocation (azimuth or elevation) in auditory projections to visual streams. l, Schematic of circular array of concentric speakers \nand LEDs. Speakers/LEDs were located 18° apart, spanning  -108° to 108° in azimuth and -36° to 36° in elevation. Inset \n(bottom right) shows stimulation protocol and schematic of bandpass filtered noise bursts (Methods). m, Example location \nresponse areas (LRAs) of sound-location-selective AC →VC boutons. n. Percentages of noise-responsive boutons that \nwere selective for sound azimuth (top) or elevation (bottom), grouped by visual area. N total=624087 boutons, N responsive = \n81559 boutons, Nazimuth.selective = 15556 boutons, Nelevation.selective = 7793 boutons, 13 animals. o,p. Same as g,h, comparing \nAC bouton LRAs across areas and streams. q, Same as h, for correlations in azimuth (left) or elevation (right) tuning \ncurves between boutons of the same or different streams.  \nSound location encoding segregates into visual streams \nThe auditory cortex contains spatially intermingled neurons that are tuned to sound location 44,45. Recent work \nhas shown these AC-neurons provide sound-tuned inputs to visual cortex and that their inputs are not \ntopographically organised within V1 29. Therefore, it remains unclear whether there is any organisational logic \nto sound-tuned inputs to the VC. Notably, while the visual cortex is retinotopically organised, the representation \nof visual space is not uniform across visual areas. As a result, the dorsal and ventral streams are biased \ntowards the lower and upper visual fields, respectively 13,46. One intriguing possibility is that sound location \ninformation is transmitted differently to the two streams, potentially aligning with the retinotopic bias in each \nstream (Fig. 3j). This matching of sound and visual location could facilitate the binding of multisensory stimuli \nbased on spatial co-localisation, as observed in subcortical regions such as the superior colliculus47,48. To test \nthis hypothesis, we characterised tuning to sound location in AC→VC inputs. Mice were head-fixed at the \ncentre of a spherical speaker array and presented with broadband noise stimuli originating from different \nazimuth and elevation locations, while we imaged AC→VC inputs (Fig. 3k-m). In total, 19% of sound-\nresponsive boutons were tuned to sound azimuth, while 10% were tuned to sound elevation. The fractions of \nazimuth tuned boutons varied across VC areas, with boutons in areas AL and LI most frequently displaying \nazimuth selectivity (p%azimuth selective = 0.019, p%elevation selective = 0.059, mixed effects ANOVA, Fig. 3n). \n \nTo assess the similarity in sound location representation between areas, we computed pairwise signal \ncorrelations between location tuning profiles of sound-responsive boutons and grouped them by areas and \nstreams. In contrast to the representation of sound frequency, sound location responses of boutons projecting \nto areas of the same stream exhibited higher signal correlations than the responses of boutons projecting to \ndifferent streams (Fig. 3o-p). This trend was also present when only considering responses to different \nazimuths or elevations, but did not reach statistical significance (Fig. 3q, Suppl. Fig.9a). This suggests that \nsimilarities in location tuning in projections to the same stream arise through a combination of azimuth and \nelevation tuning and raises the intriguing possibility that azimuth and elevation preference might vary between \nAC axons targeting different HVAs. (Fig. 3q, Suppl. Fig.9a). Furthermore, this stream-wise organisation was \nalso observed at the level of axons (i.e., when boutons from the same axon were grouped, see Methods, \nSuppl. Fig.9b), which may more closely reflect the anatomical segregation uncovered in our MAPseq \nexperiments (Fig. 2). Thus, sound location tuning segregates in a stream-like manner, indicating that the \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 12 \nanatomically segregated auditory projections to the two visual streams revealed with our MAPseq experiments \n(Fig. 2j) are functionally specialised. \nRepresentation of sound location in AC→VC projections \nOur previous findings indicate that AC projections differentially transmit sound location information across the \nVC. Therefore, we separately analysed the representation of sound azimuth and elevation in AC inputs to \ndifferent VC areas, as information related to these two spatial dimensions might be differentially distributed \nacross the VC. Most AC→VC boutons were tuned to sounds on either the ipsilateral or contralateral side, with \na smaller proportion tuned to central sounds, resulting in a trimodal distribution of sound azimuth tuning (Fig. \n4a). Azimuth tuning curves were relatively wide, particularly in boutons tuned to ipsilateral and contralateral \nsounds, consistent with previous descriptions of azimuth representations in auditory cortex 44,45,49,50 (widthipsi-\ntuned = 105.5°; width contra-tuned = 97.2°; width centre-tuned = 43.7°; Suppl. Fig.9c). The shape of the azimuth tuning \ndistribution differed between visual areas, specifically between primary and posterior visual areas (V1, P, POR) \nand anterior areas (RL, A, AM, Fig. 4b). Most strikingly, these areas differed in the fraction of centre-tuned \nboutons they receive, which was higher in dorsal stream areas than in ventral stream areas and V1 (Fig. 4d-f, \ndorsal vs ventral, p = 0.002, mixed effects ANOVA)  Thus, inputs to V1 and posterior ventral areas were mainly \ntuned to ipsilateral or contralateral sounds, forming a bimodal distribution, while inputs to dorsal stream areas \nwere more evenly distributed across ipsilateral, centre and contralateral tuning (Fig.4b-f and Suppl. Fig. 9d). \nIn addition to differences in sound azimuth representation, AC inputs to the two streams also varied in their \nelevation tuning. Overall, AC→VC boutons were uniformly tuned to all elevations, with a small bias towards \nhigh elevation sounds (Fig. 4g). However, the mean preferred elevation differed between areas: AC boutons \nin ventral stream areas were tuned to higher elevations than boutons in dorsal stream areas (Fig. 4ih-k, dorsal \nvs ventral, p = 0.027, mixed effects ANOVA). Thus, AC inputs encode both sound azimuth and elevation \ndifferently depending on their target visual stream. Notably, location tuning in AC→VC boutons did not differ \nbetween animals with anterior and posterior injection sites (Suppl. Fig. 10a-g), indicating that stronger \nprojections from anterior and posterior AC to dorsal and ventral streams are unlikely to account for differences \nin location tuning. Interestingly, there was a relationship between the representation of sound azimuth and \ndorso-ventral injection location, ventral-injected animals having fewer centre-tuned boutons than dorsal-\ninjected animals (Suppl. Fig. 10h-n). This difference did not account for our earlier results, since dorso-ventral \ninjection location was incorporated into all linear mixed models, and differences in percentages of centre-tuned \nboutons across areas were present in both dorsal-and ventral-injected animals (Suppl. Fig. 10k). \nThe presence of biased representations of sound location in AC →VC projections  raises the question of \nwhether these biases align with the retinotopic biases of the target areas. To evaluate this possibility, we first \ninjected the red calcium indicator jRGECO1a into the visual cortex and mapped visual retinotopy at the single \nneuron level in V1 and HVAs (Fig. 4l). As expected, retinotopic maps obtained from L2/3 neurons in HVAs \nexhibited biases in their representations of azimuth and elevation 12,13 (Fig. 4m,n). We then compared the \nrepresentations of visual space in VC neurons and auditory space in AC boutons.  \nTo compare the representation of stimulus azimuth in these neural populations, we focused on the relative \nrepresentation of frontal (i.e. central) stimuli, as this feature varied most strongly across the AC projections to \nthe different visual areas. The percentage of centre-tuned AC boutons correlated positively with the percentage \nof centre-tuned VC neurons across the visual cortex (r = 0.41, p = 4.46x10 -4, Spearman correlation, Fig. 4o). \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 13 \nThis positive correlation was also found within the primary visual cortex (Suppl. Fig. 9e, r = 0.49, p = 0.004), \nand suggests that the proportion of centre-tuned AC inputs to a given region of VC is tailored to its retinotopic \npreferences. Notably, we found no consistent relationship between the best sound azimuth in AC boutons and \nbest visual azimuth in VC neurons, including within V1, consistent with previously published findings 29 (Suppl. \nFig. 9f). However, we did observe that the difference (Δ) between preferred sound azimuth in AC boutons and \nmean preferred visual azimuth in VC neurons in areas A, AM and PM was lower than in the respective shuffle \ncontrols (Fig. 4p), suggesting that AC inputs to medial visual areas are aligned with the retinotopic preferences \nof their targets. The representation of stimulus elevation in AC boutons and VC neurons was also correlated, \nboth at the level of the whole VC and across higher visual areas (Fig. 4q,r). Accordingly, the mean distance \nbetween the preferred sound elevation in AC boutons and the preferred visual elevation in VC neurons was \nlower than in a shuffle control in all HVAs except for area A (Fig. 4s). Thus, auditory inputs to the VC convey \nauditory spatial information that is aligned with the retinotopic preferences of their target areas.  \nFigure 4. Location tuning in AC →VC inputs is biased towards retinotopy of target HVAs. a, Distribution of best \nsound azimuth for all azimuth-selective boutons that were well fit with a Gaussian ( r2 > 0.6). Boutons were subsequently \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 14 \ngrouped into ipsilateral, centre or contralateral tuned (red lines). b, Distribution of best sound azimuth for AC boutons in \ndifferent visual areas (colored). Gray distributions are for all boutons combined. c, Distribution of best sound azimuth for \nAC boutons in dorsal and ventral streams. d, Percentage of centre-tuned (between -30° and 30°) boutons in the two visual \nstreams. Each circle corresponds to one session, thick lines indicate median across sessions. P-value obtained from linear \nmixed model. e, Schematic of two-photon imaging of auditory projections to visual cortex.  f, Left, Percentage of centre-\ntuned boutons per recording, grouped by visual area. Each circle is one recording, horizontal bars indicate median across \nrecordings. Gray p-value indicates comparison across all areas (mixed effects ANOVA). Right, percentage of centre-tuned \nboutons per spatial bin (300 um/bin), aligned across animals. Gray color (dotted line in scale-bar) indicates chance level. \ng, Distribution of best elevation for all elevation-selective boutons. h, Distribution of best sound azimuth for AC boutons in \ndifferent visual areas (colored). i, Distribution of best elevation for boutons in the dorsal and ventral streams. j, Best sound \nelevation per recording for boutons in dorsal and ventral streams. P-value obtained from linear mixed model. k, Left, mean \nbest sound elevation per recording, grouped by visual area. Gray p-value indicates comparison across all areas (mixed \neffects ANOVA), black p-values indicate comparisons between area pairs (Mann Whitney U test). Right, average best \nsound elevation per spatial bin. l, Top, schematic of viral injections to express jRGECO1a in visual cortex neurons. In some \ncases, both indicators were expressed in the same animal. Bottom, example responses to flashing LED stimuli in different \nlocations of two VC neurons. Retinotopy was only mapped on the contralateral side. m, Left, average best visual azimuth \nfor VC neurons per recording, grouped by visual area. Right, percentage of centre-tuned (< 30°) VC neurons per spatial \nbin on aligned cortical map. n, same as m, for best visual elevation in VC neurons. o, Correlation between percentage of \ncentre-tuned VC neurons and percentage of centre-tuned AC boutons in each spatial bin. R and p value obtained with \nSpearman correlation. p, Absolute difference (Δ) between best visual azimuth of each area (VC neurons) and the best \nsound azimuth of each recording (AC boutons). Colored bars indicate median distance per area, gray bars indicate \ndistance after shuffling AC boutons across areas. Asterisks indicate significant difference between measured distances \nand shuffle controls (Mann Whitney U test, *: p< 0.05, **: p < 0.01, ***: p < 0.001). q, Correlation between best visual \nelevation in VC neurons and best sound elevation in AC boutons in each spatial bin. R and p value obtained with Pearson \ncorrelation. r, Relationship between average best visual elevation VC neurons and best sound elevation in AC boutons \nacross HVAs. R and p-value obtained with Pearson correlation. s, same as p, for absolute difference in stimulus elevation.  \nDiscussion \nOur results reveal a fundamental principle of cortico-cortical communication, whereby cross-modal information \nis differentially broadcast according to the organising principles of the target area. We found that auditory \ncortex projections to visual cortex are anatomically segregated, following the division of the visual cortex into \ntwo processing streams. Neurons in anterior AC preferentially target dorsal stream areas while neurons in \nposterior AC target ventral stream areas. Moreover, individual AC neurons exhibit stream-specific co-\nprojection patterns, preferentially targeting multiple areas within the same stream. This anatomical segregation \nat the population and single-neuron level results in differential information transfer from auditory cortex to visual \ncortex, such that auditory spatial information is differentially distributed to the two visual processing streams. \nFurthermore, the auditory spatial information in AC→VC projections is biased towards the retinotopic location \nof their target, thus aligning with the intrinsic functional organisation of the visual cortex. \nThe differential representation of specific cortical co-projection motifs was previously found in V1→HVA \nprojections40, suggesting that specificity in co-projection patterns may be a common feature of cortico-cortical \ncommunication. These results indicate that similar rules may govern the co-projection patterns of V1 and AC \nneurons across HVAs. Bifurcating AC→VC projections provide a mechanism for broadcasting the same \nauditory information within, but not across, visual processing streams. Such structured connectivity at the \nstream level could ensure specificity while facilitating parallel processing of multisensory information across \nfunctionally distinct visual areas within each stream. AC neurons projecting within a stream also exhibit greater \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 15 \nsimilarity in their co-projection patterns to other cortical targets beyond the visual cortex. This suggests that \nthese specialised output channels are likely to extend to other sensory modalities providing the basis for unified \ncortical multisensory representations. \nOur finding that sound-location tuning in AC inputs is spatially biased towards the retinotopy of their target \nvisual area is consistent with the organisation of feedback projections within the visual cortex, which tend to \npreserve local retinotopy 51–53. Furthermore, our findings align with the principle of spatial coincidence as a \nhallmark of multisensory integration, extensively documented in subcortical structures such as the superior \ncolliculus28,54. The relatively coarse alignment of auditory tuning with retinotopic location may reflect the broad \nspatial receptive fields of auditory cortical neurons compared to the superior colliculus 44,45,49,50. Interestingly, \nour results provide functional evidence for auditory biases along the elevation axis between the dorsal and \nventral streams 12,13, extending visual stream-specific elevation differences into the auditory domain. Further \nsupporting the role of retinotopic bias in cortical multisensory interactions, neurons in area RL encode spatially \ncoincident visuo-tactile information from proximal space 55,56 and contribute to the generalisation of learned \nassociations across sensory modalities57. Consistent with this, our data show that RL receives auditory inputs \nbiased to centre azimuths and middle elevations. These results beg the question of how cross-modal cortico-\ncortical projections to higher sensory areas contribute to multisensory perception and behaviour, particularly \nin tasks requiring spatially aligned auditory and visual processing.  \nDespite the anatomical segregation of AC neurons projecting to the dorsal and ventral streams, we found no \nevidence for tonotopic organisation of AC→VC projections. This may be due to the widespread expression of \nour calcium indicator, which may cover multiple tonotopic regions in the AC, the local salt-and-pepper \norganisation of frequency tuning in AC 58, and the challenges of measuring global tonotopic organisation from \nsupra-threshold responses in the mouse AC 43. Together, our MAPseq and functional results suggest that the \nrepresentation of sound location may vary across different anatomical regions of the auditory cortex, for \nexample between dorsal and ventral auditory cortex (Suppl. Fig.10), suggesting that the auditory cortex \ncontains a map of auditory space. However, no physiological study to date has identified such a map. Instead, \nsound-source locations are thought to be represented by widely distributed populations of broadly tuned \nneurons45,59. Our findings raise the possibility that a cortical map of auditory space could emerge in the AC \nwithin the subset of sound location-encoding neurons that project to the VC.  \nThe anatomically structured and functionally specialised cross-modal projections to the visual cortex we \nidentified may develop under the influence of visual experience, analogous to the role of visual retinotopy in \naligning visual and auditory maps in the superior colliculus during development 47,60. The visual critical period \noverlaps with the development of binaural integration 61, and direct VC →AC projections have been proposed \nto gate auditory critical periods by regulating the impact of visual experience on auditory processing 62. \nTogether, our results demonstrate that multisensory cortico-cortical communication follows the functional \norganisation of the target sensory system. While sound location information is functionally segregated, the \nuniform representation of sound frequency across visual areas may ensure that all visual regions receive a \ncomplete auditory spectral representation, facilitating robust integration of auditory signals into visual \nprocessing. This suggests that during multisensory behaviour, cross-modal cortico-cortical projections may \nsupport perceptual processing of visual and auditory stimuli originating from the same spatial source, providing \na mechanistic basis for multisensory integration in higher cortical visual areas. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 16 \nMethods \nAnimals \nAll animal procedures were licensed by the UK Home Office and approved by the Crick Institutional Animal \nWelfare Ethical Review Panel (PEB4A5081 & PP2817210). Experiments were performed on a total of 21 mice \n(male and female) of the C57BL/6J.Cdh23753A>G 63 (MRC Harwell, UK) strain, aged 2-4 months, including 3 \nmice used for MAPseq experiments and 18 mice used for calcium imaging experiments. Mice were bred and \nmaintained at the mouse facility of the Francis Crick Institute, with controlled temperature (21 ± 2 °C) and \nhumidity (55 ± 10 % RH), with ad libitum access to food (Teklad global diet, Envigo, UK) and water, and kept \non a 12 h:12 h light/dark cycle (lights on at 10 pm).  \nSurgeries \n24 hours before surgery, mice were given an analgesic (Metacam in custard, 1.5 mg/ml, 1:1 Metacam: \ncustard). Mice received a subcutaneous injection of buprenorphine (0.1 mg/kg) and meloxicam (10 mg/kg) \nimmediately before surgery. Mice were then anaesthetised with isoflurane (1%-5%), and their heads were \nshaved. They were then placed on a heating pad under a stereotaxic frame (Kopf Instruments). The scalp was \ncleaned using a chlorhexidine solution (0.8%). The mouse’s temperature was maintained at 35-37°C during \nsurgery using a DC temperature controller (FHC) with a rectal thermometer input and a heating mat output. \nAnaesthesia depth was monitored by observing  the respiratory pattern. The mouse’s eyes were covered with \neye ointment (Maxitrol) to prevent desiccation. All injections were performed using glass microinjection \nneedles, using a pressure microinjector (Nanoject III, Drummond). All surgeries were performed on the left \ncranial hemisphere.  \nFor MAPseq experiments, the scalp was incised with a scalpel, and the temporal muscle was detached from \nthe skull to expose the squamosal bone. The animal’s head was tilted clockwise by ~20° along the roll axis, \nand the microinjector was angled at 20° counterclockwise to ensure that the injection was performed nearly \nperpendicular to the brain surface. The MAPseq Sindbis virus library (JK100L2, MAPseq/BARseq Core facility, \nCold Spring Harbor Laboratory) was injected through a small craniotomy into the auditory cortex. Each animal \nreceived 3 injections, placed 0.8, 1.2 and 1.6 mm anterior to the juncture of the parietal, interparietal and \nsquamosal skull bones, and dorso-ventrally positioned just below the temporal muscle’s attachment point to \nthe skull. Injection depth ranged between -500 and -250 µm from the pia, with 150 nl per injection (100 nl at –\n500 µm and 50 nl at –250  µm, injection rate: 1 nl/s). After injections, the craniotomy was sealed with VetBond \n(3M), and the scalp was sutured.  \nFor in vivo imaging experiments, mice received an intraperitoneal injection of dexamethasone (2-3 mg/kg), 3-\n5 hours before surgery. A portion of the scalp was removed bilaterally, access to the auditory cortex was \nobtained by detaching the temporal muscle and rotating the head as described above. 100 nl of AAV1-hSyn-\njGCaMP7b (Addgene) were injected 1.4-1.6 mm anterior to the juncture of the parietal, interparietal and \nsquamosal skull bones, at a depth of 500 µm. The head was then rotated back to a horizontal position, and a \ncustom-made circular metallic head-fixation plate was attached to the skull above the visual cortex using dental \ncement (SuperBond C&B, Sun Medical). A circular craniotomy (4 mm diameter) was performed above the \nvisual cortex using lambda as a landmark;  the skull was removed, and the dura was carefully resected with \nforceps. In some animals (11/18), to allow imaging of neural activity in the visual cortex, 5 to 8 injections of \nAAV1-hSyn-jRGECO1a (Addgene) were made across the visual cortex, using blood vessels as landmarks. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 17 \nFor these injections 50-70 nL were delivered at each location, at ~250 µm depth. In all animals, the craniotomy \nwas covered with a glass coverslip (4 mm diameter), and secured with VetBond and dental cement. Animals \nwere allowed to recover for a week before intrinsic imaging. Two-photon imaging commenced 3 weeks after \ninjections. \nSpike-in RNA \nSpike-in RNA was in vitro transcribed from Ultramer® duplex oligo (Sequence 5’-3’: \nATGATCATAATACGACTCACTATAGGGGACGAGCTGTACAAGTAAACGCGTAATGATACGGCGACCACC\nGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNNNNNNNNNNNNNNNNNNNATCA\nGTCATCGGAGCGGCCGCTACCTAATTGCCGTCGTGAGGTACGACCACCGCTAGCTGTACA, Integrated \nDNA Technologies) as described previously 30 using MEGAscript T7 Transcription kit (Invitrogen, #AM1333) \nfor MAPseq optimisation, and T7 MEGAshort kit (Invitrogen, #AM1354) for A1 MAPseq experiments. We \nincluded the DNAse treatment step after in vitro transcription to remove template DNA. The in vitro transcribed \nRNA was purified using the RNeasy Mini Kit (QIAGEN, #74106) and stored in aliquots at -80 oC. \n \nMAPseq tissue preparation and laser capture microdissection \nTwo days after injection, mice were culled by intraperitoneal injection of pentobarbitone (1.4 mg g -1), the brain \nwas dissected and flash frozen in Optimal Cutting Temperature (OCT) compound within a cryomold over 100% \nethanol on dry ice and stored at -80 C. Coronal brain sections (200 m thick) were cryosectioned onto PEN-\nmembrane slides (Leica Microsystems #11600289). Immediately after mounting, sections were fixed for 3 min \nat 4 C with ice-cold ethanol. Sections were then rinsed with ice-cold water and stained twice in ice-cold 0.5% \ntoluidine blue for 30 s (Thermo Scientific, #348600250). Samples were rinsed three times in ice-cold water, \nfixed twice in ice-cold 75% ethanol at 4 C for 2 min, and dried in a vacuum desiccator for 30 mins at room \ntemperature. The samples were then stored at -80 C in a 50 ml falcon tube with desiccant. Laser capture \nmicrodissection (LCM) was performed using the Leica LMD 7000. Cortical areas of interest were dissected \ninto approximately 600 m arc-length cubelets. In addition, subcortical targets of the auditory cortex, including \nthalamus, striatum, and tectum, as well as regions of the olfactory bulb as a negative control, were dissected \nby LCM. Equivalent areas in adjacent sections were typically pooled generating cubelets approximately \n400m thick. The primary auditory cortex was further dissected into upper and lower layers of approximately \n300m thickness for each half. For all LCM samples, dissections aimed to avoid regions containing fibres of \npassage. Samples were collected into 0.5 ml tubes (Greiner Bio-One, #682201) containing 65 l of buffer RLT \n(QIAGEN, #74004) with 40 mM Dithiothreitol (DTT, Promega, #V3151), placed on dry ice and stored at -80 \nC. LCM samples were homogenized by incubating for 5 min at room temperature in buffer RLT with DTT (350 \nl total volume), combined with physical disruption using a pipettor. RNA was extracted using RNeasy Micro \nkit (QIAGEN, #74004) without the DNA digestion step and eluted in 14 l water. RNA quality was assessed \nusing a TapeStation D1000 (Agilent) on 1 l of each sample. \nMAPseq sequencing library preparation \nPCR primer sequences for MAPseq library preparation were adapted for compatibility with the NovaSeq \nsequencing platform. For each reverse transcription reaction, 8 l of RNA eluate per LCM sample was \ncombined with 1x10 -7g/l spike-in RNA, 1 l 10mM dNTPs (Thermo Scientific, #10319879), 2 l water, and \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 18 \n1 l of 10 M reverse transcription primer (MAPSEQ_NOVA_RT### in the primer table list, the oligo sequence: \nTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNNNNNNNNNXXXXXXXXXXXXXXXXTGTACA\nGCTAGCGGTGGTCG contains a unique 16 nucleotide barcode sequence (X 16) , enabling assignment of \nsequencing reads to different LCM samples, as well as a random 14 nucleotide UMI). The reaction was heated \nto 70 C for 10mins, placed on ice for 5 mins, and 4 l  Superscript IV buffer, 1 l 100mM DTT, 1 l \nRNaseOUT (Thermo Fisher Scientific, #10777019), 1 l SuperScript IV reverse transcriptase (Thermo Fisher \nScientific, #18090050) were added to the RNA mix and incubated at 55 C for 50 minutes and 80 C for \n10 minutes. 1 l RNAse H (New England Biolabs, #M0297L) was added and incubated at 37 C for 20 minutes. \nSecond strand synthesis was then performed, adding 1.5l of 10 M targeted second strand synthesis primer \n(MAPseq_NOVA_SSS_006), 5 l 10X reaction mix, 0.4 l AccuPrime Pfx DNA polymerase (Thermo Fisher \nScientific, #12344032) and 22.1 l water, the reaction was heated to 95 C for 2 mins 15 sec, annealed at 55 \nC 1 min, extended 68 C 1 min. All samples were then pooled together, and bead clean-up performed using \nKAPA Pure Beads (Roche, #KK8001) using 1.8x beads ratio then eluted in 10 l water per number of samples. \n80% of the eluted pooled second strand synthesis product were then split across 96 well PCR plates. Per well, \n8 l of the double stranded cDNA eluate was exonuclease treated by adding 1 l Thermolabile Exonuclease I \n(New England Biolabs, M0568L) and 1 l of buffer r3.1 and incubated in a thermocycler for 30 mins at 37 C \nfollowed by 5 mins at 80 C. ExoI-treated cDNA was then amplified by nested PCR. First, 10 l of the ExoI \nreaction was amplified using Accuprime pfx polymerase (Thermo Fisher Scientific, #12344032) and primers \nMAPseq_NOVA_SSS_006 and MAPseq_NOVA_PCR1_007 in a 50 l volume. PCR cycle conditions were: 2 \nmins at 95 C, followed by 20 cycles of 95 oC for 15 seconds and 68 oC for 1 minute. 10 l Thermolabile \nExonuclease I (New England Biolabs, M0568L) was then added to the PCR reaction and incubated for 30 \nmins at 37 C followed by 5 mins at 80 C. Samples were pooled again and 1/3 of the PCR product was purified \nusing KAPA Pure Beads (Roche, #KK8001) with 1.5x bead ratio and eluted in 11 l water multiplied by number \nof samples and split across 96 well plates using the same number of wells as there were samples. In each \nwell, 10 l of the eluate was used in a second PCR reaction (primers: MAPseq_NOVA_PCR2_008, \nMAPseq_NOVA_PCR2_009) using AccuPrime pfx polymerase (Thermo Fisher Scientific, #12344032), PCR \ncycle conditions: 2 mins at 95 C followed by 8 cycles of 95 C for 15 seconds and 68 C for 1 minute. The \nfinal PCR product was purified using Monarch PCR & DNA Clean-up Kit (New England Biolabs, #  T1030L) \nand run on a 1.5% agarose gel. The ~250bp band was gel extracted using Wizard SV Gel and PCR Clean-Up \nSystem (Promega, #A9281). DNA sequencing was performed with 500 million reads per mouse using \nNovaSeq 6000 or NovaSeq X (Illumina) . For one mouse (FIAA55.4d), 9 l RNA was used per LCM sample \n(rather than 8l) in the 1st strand synthesis reaction, together with a 10% increase in second strand synthesis \neluate used after bead clean-up and corresponding increase in number of PCR reactions.  \nRegistration of laser capture microdissection cubelets to the Allen Common Coordinate Framework \n(CCF). \nTo register LCM cubelets to the Allen Common Coordinate Framework (CCF) v3 31, overview images of the \nsection were acquired before LCM and after each LCM cubelet had been dissected. Each post-LCM cubelet \nimage was aligned with the original section overview image using a custom MATLAB script through automated \nSURF-based feature mapping, or control-point registration if automated feature mapping was not successful. \nCubelet boundaries were manually annotated on aligned pre- and post-LCM images.  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 19 \n \nThe overview coronal sections were registered to the Allen CCF v331 using QuickNII and VisuAlign software64. \nThese data were then used to transform each pixel in each LCM slice into Allen CCF coordinates in Python. \nTo calculate the Allen CCF coordinates for each LCM cubelet, we then used the binary mask to extract Allen \nCCF coordinates from each 3D coronal slice and annotate every coordinate in the LCM sample to with the \ncorresponding coordinates in the 25 m resolution Allen CCF volume. Larger non-linear deformations – \nparticularly those arising from separation of cortical hemispheres across the midline – led some pixels to be \nerroneously assigned to the contralateral hemisphere, as well as occasional missing pixel assignments. We \ncorrected these artifacts by removing erroneous contralateral assignments and filling any holes contained \nwithin LCM sample annotation.  \nMAPseq sequencing data pre-processing. \nAll barcode sequencing data analysis was performed in Python. Raw barcode counts were comprised of two \nfastq files each with 100bp reads for either end of the MAPseq PCR amplicon. These reads were combined \nand filtered to select reads containing the expected consensus sequence GCGGC at the positions 37 to 42 of \nthe read. Reads were assigned to the LCM samples they originated from with fastx_barcode_splitter, using \nthe unique 16-nucleotide sample barcode sequence specified in the gene-specific reverse transcription primer, \nallowing for a maximum of two mismatches. We corrected for sequencing and PCR errors for the 30 nucleotide \nneuron barcode sequences in each sample using the UMI-tools API 65 employing the directional clusterer \napproach. Subsequently, the 14 nucleotide UMI sequences for each neuron in each sample were similarly \ncorrected for sequencing/PCR errors using directional clustering in the UMI-tools API. To further reduce the \nlikelihood of including reads arising from sequencing errors, we applied a minimum UMI-duplicate count \nthreshold for neuron barcode reads. Thresholds were determined by visually inspecting UMI-duplicate count \nhistograms for each mouse dataset, which showed a bimodal distribution with a smaller peak at low UMI counts \nindicative of potential spurious reads. The cut-off was chosen to exclude this low-count noise population. \nConsequently, cut-off values of 5, 4, and 2 UMI duplicates were used for mice FIAA45.6a, FIAA45.6d, and \nFIAA55.4d, respectively. \n \nTemplate switching has previously been reported as a complication arising from pooling multiple samples \nduring the MAPseq library preparation 32. This occurs during PCR amplification when the DNA polymerase \ndetaches mid-elongation, and the truncated product subsequently anneals to a different template strand that \nshares the same sequence, resulting in chimeric PCR amplicons 66, Suppl. Fig. 1c). To correct for template \nswitching events, we exploited the high sequencing depth made achievable by adapting MAPseq for the high-\nthroughput NOVAseq platform. We firstly identified instances where the same UMI sequence was shared by \ndifferent neuron barcodes. We reasoned that shared UMI sequences across neurons could be due to: (i) re-\nuse of the same UMIs from biases in random nucleotide selection during oligo synthesis, (ii) low quality reads \nwith low complexity sequences caused by sequencing artifacts, (iii) template switching events. Since template \nswitching events are a rare occurrence during a PCR amplification cycle 66, the original neuron-barcode/UMI \ncombination would be expected to be present in much higher abundance compared to the template switched \nneuron-barcode/UMI combination. For each re-used UMI we therefore calculated the ratio of the neuron-\nbarcode/UMI duplicate count for the most abundant neuron-barcode/UMI combination (1st max) and compared \nit to the next most abundant neuron-barcode/UMI duplicate count (2nd max). For 1st max/2nd max ratios of less \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 20 \nthan 10, we observed an increase in AT-content and a decrease in entropy for UMI sequences (Suppl. Fig. \n1d), suggesting these were likely due to factors (i) or (ii). Consequently, shared UMIs with a 1 st max/2nd max \nratios below 10 were discarded, while shared UMIs with ratios > 10 were considered template switching events. \nIn these cases, we retained the 1 st max neuron-barcode/UMI combination while discarding all the neuron-\nbarcode/UMI combinations that were less abundant.  \nNext, all neuron barcode UMI counts were deduplicated and separated from barcodes corresponding to spike-\nin RNA by the spike-in specific barcode sequence: N 24ATCAGTCA. Finally, neuron barcode counts per LCM \nsample were collated into a single matrix.  \nMAPseq data filtering, normalization and soma identification. \nNeuron barcodes that were present at less than 100 barcode counts across the whole dataset were discarded. \nComparing barcode counts in A1 target samples to the OB negative control samples showed the vast majority \n(95%) of barcodes in the negative control were present as single counts (Suppl. Fig. 1e). We therefore \nconsidered this background and only included barcode counts in a sample if present at >2 counts. Next, as a \nquality check to identify LCM samples with poor RNA yield, samples where the total MAPseq neuron barcode \ncount was zero were removed. Barcode counts were then normalised in two steps. First, for each sample we \ncalculated a scaling factor by dividing that sample’s spike-in RNA count by the median spike-in count across \nall samples. Next we divided MAPseq neuron barcode counts in each sample by that sample’s scaling factor, \nthereby correcting for variation in library preparation efficiency across samples.  \n \nTo subsequently identify which samples contained the neuron’s soma for each barcode, we took a similar \napproach to BRICseq32. For every neuron barcode we identified the sample containing the maximum amount \nof that barcode (somamax). We then identified the sample containing the next largest number of counts for that \nbarcode (2ndmax), excluding samples adjacent to the somamax sample in 3D space as they were likely to contain \ndendrites and local axonal projections. We considered soma max to be a soma-containing sample only when \nthe somamax/2ndmax count ratio was >5. Barcodes where somamax/2ndmax count ratio <5 were discarded. We then \nfiltered for neurons that contained a minimum of 10 barcode counts in the 2 ndmax sample as well as selecting \nfor neurons where somamax LCM cubelet contained predominantly A1. \nComparison of A1 MAPseq to anterograde tracing datasets \nTo compare mesoscale anterograde tracing datasets from A1 to the A1 MAPseq dataset, we used EGFP \nanterograde tracing data from the Allen Mouse Brain Connectivity Atlas 33. For comparison of cortex-wide \nprojection strength patterns (Fig. 1c and 1f as well as Suppl. Fig. 2b-c), we selected experiments where EGFP-\nexpressing AAV was injected into wild-type mice and >75% of the injection site was in A1 (experiment IDs: \n120491896, 116903230, 100149109; available from connectivity.brain-map.org/). For the comparison of \nanterograde tracing experiments to our MAPseq data at the cubelet level, individual anterograde tracing \nexperiments were normalised to injection volume, we then z-scored the Log10(mean Allen experiment summed \nvoxel-wise projection density) within each cortical 3D LCM cubelet ROI location and compared this to the per \nmouse z-scored Log10(MAPseq barcode counts) for each LCM cubelet. To compare our MAPseq datasets to \nanterograde tracing experiments at the level of cortical areas, we calculated the mean z-scored \nLog10(projection density) for each cortical brain area across the 3 Allen experiments, and compared this to the \nLog10(mean projection density) to each area in our MAPseq dataset. Area projection density was calculated \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 21 \nusing “homogeneous across cubelet” area assignment approach (described in the Methods section: \n“Assignment of A1 projections to cortical brain areas”). The Pearson correlation coefficient and corresponding \np value were calculated for either the summed MAPseq barcode counts for each cortical LCM sample or the \nmean projection density to each cortical area covered. \nFor comparison of projection strength to visual cortical areas based on injection location (Suppl. Fig.4b-d) \nusing data from the Allen Mouse Brain Connectivity Atlas 33, we included experiments where >95% of the \ninjection of EGFP-expressing AAV was in auditory cortex (but not necessarily A1). Injections were divided into \nanterior or posterior based on injection location (anterior: 100149109, 120491896, 184159706, 182090318, \n287223629; posterior: 116903230, 146858006, 569994739, 112881858). Some of the experiments were \ncarried out in transgenic animals and featured Cre-dependent expression of EGFP (Cux2-IRES-Cre: \n184159706, 569994739; Rorb-IRES2-Cre: 287223629; Rbp4-Cre_KL100: 182090318). Similar trends were \nobserved when only including WT mice. Assignment of MAPseq neuron projection patterns to brain areas. \nAverage projection strengths to visual areas were normalised within each animal.  For analysis of relationship \nbetween A-P position of AC injection and projection strength to VC areas, a linear regression model was fit for \nall VC areas simultaneously using the glm function in the Statsmodels Python package . The model included \nEuclidean distance to each VC area as an additional fixed effect, in order to account for inter-areal distance. \nThe resulting p-values were corrected for multiple comparisons using the Bonferroni approach. \n \nNeuron projection assignment to broadly grouped brain regions  \nTo calculate projection scores to broadly grouped brain areas (Fig. 1d), samples containing the cortex, \nthalamus, superior colliculus, inferior colliculus, and striatum were manually annotated during dissection. \nCounts were pooled across samples in brain region across annotated groups. Hierarchical clustering was \nperformed using the clustermap function in the Python seaborn package 67 with the canberra distance metric, \nwith projection score determined as range normalised neuron barcode counts (subtracting minimum and \ndividing by range).  \nAssignment of A1 projections to cortical brain areas \nSingle neuron projection patterns to specific brain areas within the cortex were computed for IT neurons, \nidentified as the neurons that did not project to either the thalamus or tectum. To assign barcode counts in \ncortical LCM cubelets to cortical areas, we employed an approach that assumed barcodes were distributed \nhomogeneously across LCM cubelets and distributed barcode counts within a cubelet based on the fraction of \nthe cubelet registered to each area. To calculate projection density to each area, we then summed the \nassigned neuron barcode counts for each area and divide by total area volume sampled. To this end, we \nconstructed a matrix A containing samples and the volumes of brain areas contained within each sample, \nexcluding areas that made up <10% of the cubelet. Since we were only interested in axonal projection patterns, \nwe removed samples in the neuron barcode matrix that contained majority A1. We then computed matrix ( F) \ncontaining proportion of each brain area as a fraction of the total volume of each LCM cubelet: \n \n𝐹௞௝ = 𝐴௞௝\n∑ 𝐴௞௝\n௡\n௝ୀଵ\n \n                                                                    (Equation 1) \n \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 22 \nWhere Fkj is the element in the fraction matrix F at the kth cubelet and jth area, Akj is the element in the area \nvolume matrix A for the kth cubelet and jth area, with n representing the number of areas in matrix A. We then \ngenerated a weighted fraction matrix (W) and computed the matrix (P) of projection strengths for each neuron \nbarcode across brain areas sampled by matrix multiplication: \n𝑊௜௝ = 𝐹௜௝\n∑ 𝐴௞௝\n௠\n௞ୀଵ\n \n \n𝑃 =  𝐵 ∙  𝑊 \n                     (Equation 2) \n \nWhere Wij is the element at the ith cubelet and jth area of the weighted fraction matrix ( W), Fij is the element \nat the ith cubelet and jth area of the fraction matrix (F), Akj is the element at the kth cubelet and jth area of the \nareas in matrix ( A), and m is the number of LCM cubelet samples. B denotes the matrix of barcode counts \nacross LCM samples. To normalise projection densities, values were divided by their range so that each \nneuron’s maximum projection density equalled 1. \nComparison to other cortical area-assignment approaches \nWe compared the assignment of neuron projection patterns to cortical brain areas using the above approach \nto two other area assignment approaches: the “homogeneous across area” approach and the “area is main” \napproach. For the “homogeneous across area” approach, we assumed that neuron barcode counts were \nhomogeneous across brain areas and therefore assigned neuron projection density to each area by multiple \nlinear regression. Here, we computed the projection density to each brain area as the regression coefficient \nfor the least squares solution of normalised barcode counts against area volume within each sample. We \nconstrained the regression coefficient to be non-negative and used lasso regularisation with the 𝜆 parameter \nset to 1. As with the “homogeneous across cubelet” approach, we then normalised projection densities by \ndividing by the range. \n \nFor the “area is main” approach, we assumed that barcode counts in each cubelet derived from the brain area \nthat made up the majority of that cubelet. This approach uses the same method as the “homogeneous across \ncubelet” approach, all non-majority area volumes in matrix A were set to zero. \n \nWe compared Pearson correlations between mice using different area assignment approaches and observed, \non average, a higher correlation between mice for the “homogeneous across cubelet” approach (mean \nPearson’s R = 0.87, 0.82, 0.83 for “homogeneous across cubelet”, “homogeneous across area”, and “area is \nmain approaches”; Suppl. Fig. 2d-f). \nAnalysis of single A1 neuron co-projection patterns. \nTo analyse co-projection patterns of A1 neurons to different pairs of areas, we generated projection strengths \nof individual neurons to different brain areas using the “homogeneous across cubelet” approach. \nConditional probability of co-projection.  \nTo examine how co-projection patterns of neurons vary given they already target a specific cortical visual area, \nwe calculated the conditional probability P(target|VC area) of A1 projection patterns: \n \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 23 \n𝑃( 𝑡𝑎𝑟𝑔𝑒𝑡 ∣∣ 𝑉𝐶 𝑎𝑟𝑒𝑎 ) = 𝑁(𝑡𝑎𝑟𝑔𝑒𝑡 ∩ 𝑉𝐶 𝑎𝑟𝑒𝑎)\n𝑁(𝑉𝐶 𝑎𝑟𝑒𝑎)  \n                    (Equation 3) \n \nWhere P(target|VC area) is the probability of projecting to a specific target area  given that a neuron projects \nto a particular visual area, N(𝑡𝑎𝑟𝑔𝑒𝑡 ∩ 𝑉𝐶 𝑎𝑟𝑒𝑎) is the number of neurons that project to both the target \narea the specified visual area, N(VC area) is the total number of neurons that project to that VC area. \n \nWe calculated conditional probabilities for all cortical areas present across all three mice in our dataset. To \nassess the similarity of cortical conditional projection probability patterns between different VC areas, we \nperformed a cosine similarity analysis of conditional probability vectors using the cosine_similarity function in \nscikit-learn Python package. \nOver- and under-represented motif analysis.  \nOver- and under-represented co-projection motifs were identified by comparing the number of neurons co-\nprojecting to a given pair of targets (‘observed’) with the number of co-projections expected by chance, based \non the joint probability P(AB) of observing axonal co-projections to those areas (‘ expected’).  \n \n𝑃(AB)  =  𝑃(A) × 𝑃(B) \n                      (Equation 4) \n \nWhere P(AB) is the probability of innervating both areas A and B together, P(A) and P(B) are the probability \nof innervating each area individually. ‘ Expected’ was calculated by multiplying the total number of neurons in \nthe dataset with P(AB). We then computed the effect size as log 2(observed/expected). Effect sizes were \ncompared against the null shuffle population to assess statistical significance (see below).  \nNull shuffle population comparisons. \nSince the LCM cubelets contain mixtures of different brain areas, neurons within a cubelet are biased appear \nas if they co-project between pairs of areas present within the same LCM cubelet. To distinguish genuine \nneuron co-projection patterns from these within-cubelet biases, we generated null shuffle populations that \npreserve the same within-cubelet structure. Simulation of variation in barcode labelling efficiency in MAPseq \ncan lead to artificial over-representation of projection motifs (Suppl. Fig. 3c). To generate the shuffle, we \nbinarised cubelet barcode count matrices and then applied the curveball algorithm 68 to shuffle binarised \nbarcode counts across neurons while preserving the total number of cubelet targets per neuron. This \nprocedure thus maintained both the total number of neurons targeting each LCM cubelet and the total number \nof cubelet targets per neuron, thereby preserving neuron-specific differences in barcode labelling efficiency as \nwell as the marginal probabilities of targeting cubelets. Using this approach, we performed 100,000 shuffles of \nneuron barcodes within LCM samples for each mouse. For every shuffle we reassigned projections to cortical \nbrain areas using the ‘‘homogenous across cubelet’’ approach and then performed the corresponding \nconditional probability and co-projection motif analysis to generate a null population for each analysis type. \nSince the null distribution was approximately normal, we calculated two-tailed p-values by fitting a normal \ndistribution to the shuffled data and computing the probability of observing a value at least as extreme as the \neffect observed in our data. P-values were then corrected using the Bonferroni method.  \n \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 24 \nFor visualisation of effect size in the conditional probability bubble plot (Fig. 2j), we plotted log 2(observed \nconditional probability/mean shuffled conditional probability). For the co-projection motif volcano plot (Fig. 2l-\nk), we plotted the log 2 fold change between the observed/expected co-projection motifs in the data and the \ncorresponding observed/expected values from the shuffled data. We chose log fold difference rather than a \nsimple log ratio of co ‑projecting neuron counts between observed and shuffled because shuffling preserves \nthe marginal projection probabilities of individual cubelets, but not the tendency of the same neurons to target \nthe same area across multiple cubelets. Consequently, the marginal probabilities after area assignment differ \nslightly between the observed and shuffled populations, making log fold difference of (observed/expected) in \nthe observed data versus (observed/expected) in the shuffled data a more appropriate metric for effect size. \n \nSimulation of barcode labelling efficiency \nTo examine how variation in barcode labelling efficiency can distort co-projection statistics in MAPseq \ndatasets, we simulated detection of co-projection patterns from 100,000 neurons to 10 different brain areas \nunder two efficiency scenarios: uniform or variable barcode labelling. Each neuron was given an independent \nprobability of 0.2 for projecting to any one area. In the uniform labelling efficiency condition, every projection \nwas detected with a probability of 0.5. In the variable labelling efficiency scenario, each neuron’s detection \nefficiency was an independent draw between 0 and 1. We simulated each neuron’s observed projection pattern \nby drawing a random probability between 0 and 1 for each neuron-target combination and subsequently \nrecorded a projection to that target area if the random probability value was less than the efficiency-adjusted \nprojection probability. This yielded observed neuron projection matrices used for subsequent co ‑projection \nanalyses. For each area combination, we calculated expected co-projection values as described above for \nover- and under-represented motif analysis, based on the joint probability of observing co-projections \ncalculated from total number of neurons projecting to each individual target area.  \nAnalysis of spatial relationships of A1 cortico-cortical projections in MAPseq dataset. \nExponential  decay with Euclidean distance \nTo calculate the relationship between projection frequency and projection strength (normalised barcode counts \nper mm 3 area) with distance from A1, we calculated Euclidean distances from area centroids in Allen CCF \nspace. For A1 projections to cortical areas covered by all three mice in our dataset, we fitted the change in \nprojection frequency and projection strength with Euclidean distance using the exponential decay model: \n𝑦 = 𝑎𝑒 ି௕௫  \n(Equation 5) \n \nWhere 𝑦  is the dependent variable (projection frequency or projection strength), 𝑎  is the predicted value of 𝑦  \nat 0 distance, 𝑏  is the decay constant (µm-1), and 𝑥  is the Euclidean distance between A1 and area centroids \n(µm).  \nThe exponential decay model was fitted using the optimize.curve_fit function in the SciPy Python package. To \nassess goodness of fit, we used leave-one-out cross-validation to predict the projection frequency and strength \nfor each sample and computed the p-values of the Pearson correlation coefficients between cross-validated \npredictions and observed values. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 25 \nTopographic mapping of MAPseq projection patterns \nTo examine the relationship between A-P soma position and A-P projection patterns at the cubelet level, we \ncomputed the A-P coordinates of A1 source and VC target cubelet centroids, normalised such that the most \nposterior position in VC corresponded to 0 for both source and target. VC cubelets containing >10% A1 (1 \ncubelet) were excluded to minimize confounding from local A1 projections. For each cubelet A-P position \ncontaining more than two projecting neurons , we calculated the mean A-P position of the corresponding \nsomata. To extend this analysis to the level of VC areas, we calculated the mean A-P soma position of neurons \nprojecting to each VC area (assigned using the “homogeneous across cubelet” approach) for each mouse \nindividually.  \nTo assess the relationship between targeting frequency of individual areas and A-P position, we generated \nbinary single neuron projection matrices to each VC area for each soma A-P position. Cubelets containing \nfewer than five neurons were excluded. Soma A-P position was treated as a continuous variable, and single \nneuron projections to individual VC area as a binary variable. To test whether projection probability varied with \nA-P position, logistic regression models were then fitted for all VC areas simultaneously using the Statsmodels \nPython package. Here, the logistic model relates projection probability to A‑P soma position, allowing baseline \nprojection probability and slope to differ between VC areas, and includes Euclidean distance to the target VC \narea and mouse as additional covariates.  Euclidean distance was calculated as the distance between the \ncentroids of each VC area and the A1 soma cubelet at each A-P position. This model therefore accounts for \nwithin-mouse variability in baseline projection probabilities and the effect of inter-areal distance by setting them \nas fixed effects. The resulting p-values were corrected for multiple comparisons using the Bonferroni approach. \nAdditional resources for MAPseq data visualisation. \nBrainrender was used to generate 3D videos and images of MAPseq datasets 69. To visualise data on cortical \nflatmaps, we implemented the ccf_streamlines Python package 70 , available \nfrom https://github.com/AllenInstitute/ccf_streamlines.  \nVerification of injection sites after functional imaging \nMice were euthanized with a dose of pentobarbital (80 mg/kg) and transcardially perfused with 4% \nparaformaldehyde. Brains were extracted and post-fixed overnight in 4% paraformaldehyde. Brains were \nembedded in 5% agarose and then sliced and imaged with a custom-built serial-section two photon imaging \nsystem 71, controlled by Scanimage (Vidrio Techonolgies) using BakingTray ( https://github.com/SWC-\nAdvanced-Microscopy/BakingTray). Brains were imaged at 920 nm, with slices imaged every 10 m along the \nantero-posterior axis, with a resolution of 2 m per pixel. In some cases, slices were collected afterwards and \nimaged at higher resolution on a confocal microscope (Nikon CSU-W1).  \nIntrinsic signal imaging \nIntrinsic imaging was performed under isoflurane anaesthesia. Mice were injected with acepromazine (3 \nmg/kg), ~10 min prior to imaging. Isoflurane was administered at a relatively low concentration (~0.7%) during \nimaging. The surface of the brain was illuminated at 700 nm with 2 LEDs (Thorlabs M700L4) coupled to light-\nguides (Thorlabs). Images were acquired with a CMOS camera (acA2040-120cm, Basler) through a custom-\nbuilt microscope, using a Nikon (AF Nikkor 50mm f/1.8D) objective. The objective was focused at -500 m \nfrom the brain surface during imaging. Bonsai (Lopes et al., 2015) was used for data acquisition and \nsynchronization. Visual stimuli were generated in MATLAB, and presented on an LCD screen (U2415b, Dell), \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 26 \nlocated 20 cm from the mouse’s head, covering 105° in azimuth and 76° in elevation. A checkered bar drifting \nhorizontally or vertically across the screen was presented, at a speed of 15°/s. The bar drifted 10 times in each \nof the four cardinal directions. Retinotopic maps were computed by taking the phase of the response (change \nin tissue reflectance) recorded at each pixel, relative to the phase of the drifting bar 72. Maps obtained from \nstimuli drifting in opposite directions were subtracted to correct for haemodynamic response delays.  \nTwo-photon imaging \nTwo-photon imaging was performed 3-10 weeks after viral injection, through a 16x/0.8-NA objective (Nikon) \nusing a custom-built resonant scanning two-photon microscope (INSS), controlled by Scanimage (Vidrio \nTechnologies73). A Ti:Sapphire femtosecond-pulsed laser (Chameleon Ultra II, Coherent) was used to \nstimulate the fluorophores at 930 nm (for imaging jGCaMP7b only) or 1010 nm (for imaging jGCaMP7b and \njRGECO1a simultaneously). Power post objective was between 60 and 100 mW, depending on expression \nand FOV quality. Field of view sizes were between 270x270 µm and 520x520 µm. Typically, 4 optical planes \nwere acquired quasi-simultaneously at 6.7 or 6 Hz, using a piezo actuator (Physik Instrumente) attached to \nthe objective to rapidly switch between imaging planes. Laser power was adjusted with imaging depth, to \nobtain roughly similar fluorescence levels across planes. Axons were imaged between 20 and 100 µm below \nthe pia, VC-neuron somata were imaged 50-150 µm below the pia.  \nAuditory and visual stimuli \nStimuli were generated in MATLAB (2018a, MathWorks) using custom code, and presented using \nPsychotoolbox-374. Sound stimuli were digital-to-analog converted using a soundcard (ASUS Xonar X7 or \nPEXSOUND7CH), with a 192 kHz sampling rate.  \nStimulus location experiments \nFor stimulus location experiments, auditory and visual stimuli were presented using an array of concentric \nspeakers and LEDs, organised in a 3-dimensional custom-built structure with a 20 cm radius (see Figure 3l). \nThe speakers and LEDs were all identical (speakers: VISATON, K 28 WPC BL, LEDs: 2.4 lux, 4.3°, OSRAM \nSmartLED, LWL283-Q1R2-3K8L-1). Further details of the stimulation apparatus can be found at \nhttps://github.com/Iacaruso-lab/Coliseum. A 1.5 cm diameter white acrylic diffuser was placed in front of each \nLED, to increase the size of the visual stimulus. All SPLs reported for these speakers are in presence of these \ndiffusers. Sound calibration was done using a free-field microphone (GRAS 46BE) coupled to an audio \nanalyser (APx517). The microphone was positioned 0.5 cm from the diffuser for calibration. We verified all \nspeakers produced sound at the same intensity (± 3 dB, inter-speaker variability was in same range than \nvariability due to slightly different microphone positioning relative to speaker).  \nThe sound was composed of a series of short bursts of bandpass-filtered white noise (4-24 kHz), presented \nfor 500 ms (4 bursts total, 50 ms per burst, 75 ms between bursts), with a variable inter-trial interval (1.5-2.5 \nseconds). The sounds were presented at slightly varying SPLs, centred around 65 dB (± 5 dB). Sounds were \npresented at 3 different elevations (-36°, 0° and 36°), and either 7 or 13 different azimuths, spanning the ipsi-\nand contralateral hemifields (-108° to 108°, with either 18° or 36° between speakers). Sound source locations \nwere randomised across trials. \nVisual stimuli used for retinotopic mapping consisted of a flashing white LED, presented in one location at a \ntime. The LED’s flashing pattern emulated the noise bursts in the sound stimulus (i.e 4 flashes, 50 ms duration \neach,75 ms between flashes, total length of 500 ms). Visual stimuli were presented at 3 or 5 different elevations \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 27 \n(spanning -36° to +36°, with either 18° or 36° between LEDs), and 4 or 7 different azimuths, spanning 0° to \n108° on the contralateral side (with either 18° or 36° between LEDs).  \nVisual and auditory stimuli were presented either interleaved with each other in random order, or in separate \nblocks (i.e. first a visual only block and then an auditory block), and were never presented at the same time. \nNo other light was present during the recording.  \nPure tone experiments \nPure tone stimuli were presented through an amplifier (Avisoft Bioacoustics) and played through a free-field \nelectrostatic speaker (Vifa, Avisoft Bioacoustics). The speaker was coupled to a cone, padded with cotton to \nminimise reverberations. The tip of this cone was placed ~ 1 cm from the entrance to the mouse’s ear canal, \non the contralateral side. Stimuli consisted of pure tones with different frequencies (2-64 kHz, ½ octave steps), \nwhich were presented at 3 different sound pressure levels each (40-, 50- and 60-dB SPL). Tones were \npresented in random order, for 200 ms each, with 2 seconds between trials. An LCD screen (U2415b, Dell) \nwas on at all times during these experiments, displaying a gray screen.  \nBehavioural monitoring \nDuring all recordings, videos of the face and the body were recorded at 15 Hz through two separate cameras. \nThe recording setup was illuminated with an infrared light. The face was recorded through an infrared camera \n(DMK 22BUC03), coupled to a 50 mm lens (computar). The body was recorded through an additional infrared \ncamera (Logitech). Additionally, locomotion speed was monitored using a rotary encoder (Kuebler), located at \nthe wheel’s axle. Acquisition of behavioural data and synchronisation with two-photon imaging and stimulus \npresentation was done using Bonsai.  \nImaging data analysis \nData analysis was carried out using custom code in Python and MATLAB, except were indicated.  \nQuantification of injection spread and location  \nHistology images obtained from serial two-photon tomography were assembled using StitchIt \n(https://github.com/SWC-Advanced-Microscopy/StitchIt ). Brain slices were aligned to the Allen Mouse Brain \nAtlas31 using brainreg 75 ( https://github.com/brainglobe/brainreg),  and the area of expression was then \nmanually traced using brainreg-segment ( https://github.com/brainglobe/brainglobe-segmentation ). The \nsegmented region was then used to calculate the percentage of expression in each area, and to visualise \nexpression locations in the aligned cortical flatmap (Suppl. Figure 5b). Injection centroids were taken as the \ncentre of the flatmap-projected segmented region.  \n Retinotopic map alignment \nIntrinsic imaging maps for azimuth and elevation were obtained for each animal as explained above. A \ntemplate outline of higher visual areas was manually aligned to each animal’s retinotopic maps, using known \nfeatures of area boundaries such as the reversal of retinotopy in the borders of V1-LM, V1-RL, V1-PM, AL-\nLM, and AM-PM. ROIs (regions-of-interest, boutons or somata) were assigned to  visual areas based on the \nposition of the aligned area boundaries. In cases where fields of view were on the border between two areas, \nROIs were assigned to the area they are located in. For session-wise analysis, all ROIs in a given FOV were \nassigned to the area that contained most of the ROIs. For cross-animal visualisations and analysis, the area \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 28 \noutlines were then registered across animals using affine transformation, implemented using the bUnwarpJ \npackage in ImageJ ( https://imagej.net/plugins/bunwarpj/;76). The obtained transformation matrices were then \nused to register ROI locations across animals.  \nTwo-photon imaging  \nImaging movies were registered, and regions-of-interest (ROIs) were extracted using the python \nimplementation of the suite2p toolbox 77, https://github.com/MouseLand/suite2p). Frames in which registration \nmotion was large (motion > 5 pixels) were excluded from further analysis. For somatic data, cells were \nmanually sorted based on visual inspection of the registered frames. For axonal data, the threshold for adding \nnew pixels to ROIs during the “ROI extension” phase of ROI extraction in suite2p was increased, which largely \nprevented bouton ROIs from also incorporating axonal segments and other nearby boutons. Then, putative \nboutons were automatically detected in the average registered image using a blob detection algorithm based \non a difference of Gaussians approach (python skimage library, feature.blob_dog function). The standard \ndeviation of the Gaussian kernels was empirically set such that individual boutons were recognised as blobs, \nwhile larger axonal fragments and occasional multi-bouton clusters were not. Only suite2p axonal ROIs that \nco-localised with these blobs were considered to be axonal boutons and included in further analysis. In somatic \nROIs, fluorescence was neuropil corrected, using (F = F raw – 0.7*Fneu), were Fraw was the fluorescence inside \nthe ROI and F neu was the estimate of the neuropil fluorescence. In all ROIs, fluorescence was normalised to \nthe baseline as F/F = (F-F 0)/F0, where F0 was calculated as the 30 th percentile of the fluorescence over the \nentire recording.  \nGeneralised linear model (GLM) \nBefore further analysis, a generalised linear model was fit to each boutons’s fluorescence across time, using \na set of motor and stimulus predictor variables (Suppl. Fig.7). Neural activity was baseline subtracted, and \nfluorescence intensity was binned into 100 equally spaced bins. Since the sampling rate of two-photon imaging \nis relatively low (6-6.7 Hz in these data), data was not further binned in time. Motor predictor variables \ncomprised the locomotion trace, as well as the top 30 non-laser-flicker (see above) principal components of \nthe mouse face motion. Each motor predictor variable was grouped into 10 bins for the model. Stimulus \nvariables comprised one separate variable for each stimulus ID (i.e. each stimulus location or frequency-sound \nintensity combination). In sessions with visual and auditory stimuli, each stimulus location had one auditory \nand one visual variable. All these variables had a value of 1 when the stimulus they encoded was present, and \n0 at all other times. In addition, a variable encoding time during the trial was added.  \nThe model implemented was taken from Hardcastle et al. 78 and Yoo et al. 79 and adapted for Gaussian-\ndistributed data. The model estimates the neural activity f of a bouton/neuron during time t as: \n  \n𝑓 = 𝑋௜,௧𝑤௜ \n         (Equation 6) \n \nWere Xi,t is the value of a predictor variable i at a given time point t and wi contain the weights for each variable \ni. Models were fit using the fminunc function in MATLAB, using five-fold cross-validation, and additional \nregularization for the smoothness of parameters in a continuous variable, and a lasso regularization for \nparameters in discrete variables. Model performance was assessed with cross-validated variance explained \n(cVE), calculated as: \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 29 \n  \n𝑐𝑉𝐸 = 𝑚𝑒𝑑𝑖𝑎𝑛 ൭ 1 − ቆ\n(𝑓௝ − 𝑓௣௥௘ௗ ,௝)ଶ\n(𝑓௝ − 𝑓௡௨௟௟ ,௝)ଶ ቇ ൱  \n         (Equation 7) \n \nwhere f is the neural activity of the bouton/neuron in one of the j folds, fpred,j is the neural activity predicted from \nthe model using the fit weights, and fnull,j is the mean of f. This measure is similar to R 2 in simple linear \nregression, and quantifies how close the predicted trace fpred is to the measured trace f, relative to a null \nprediction. A model with a set of predictors was considered significant if the variance explained by those \npredictors was significantly higher than zero across folds, using a one-sample Wilcoxon signed rank test.  \nMotor component subtraction \nSubtraction of the motor component was done by subtracting the fluorescence trace predicted based on the \nfitted motor variables from the measured fluorescence trace. To evaluate whether this subtraction removed \ntuning to motor variables, each motor variable was grouped into 10 or 15 equally spaced bins according to \ntheir magnitude. Tuning curves were computed for each bouton by averaging the fluorescence within each of \nthese behavioural bins. Fluorescence was z-scored before averaging by behaviour bins, and the resulting \ntuning curves were sometimes normalised between 0 and 1 for visualisation. To evaluate tuning to motor \nvariables, the same approach as Christensen and Pillow80 was used. Boutons were considered to be tuned to \na behavioural variable (locomotion, facial motion) if the variance of their fluorescence across behavioural bins \nwas significantly higher than the variance of the fluorescence shuffled across behavioural bins (Levene’s t-test \nof variance).  \nStimulus tuning analysis \nFor analysis of neural responses to stimuli, the motor component predicted from the motor linear model \ndescribed above was subtracted from each roi’s fluorescence trace. The motor-subtracted traces were aligned \nto stimulus presentation, and the response was calculated as R = Fresp – Fbase, where Fresp is the average F/F \nover the 1s following stimulus onset and Fbase is the average F/F over the 0.5-1 seconds (depending on inter-\ntrial interval) preceding the stimulus. This R value was averaged across repeated presentations of the same \nstimulus to obtain frequency-response areas (FRAs) or location response areas (LRAs)  \n \nNeurons were classified as responsive if the Fresp across trials for the stimulus type evoking the strongest mean \nFresp (stimulus location or frequency-SPL combination) was significantly higher than the Fbase (one sided \nWilcoxon signed-rank test) and the mean Fresp  for that stimulus exceeded 1 F/F. Neurons were classified as \nazimuth or elevation-selective if they were responsive and exhibited a significant change in response with \nazimuth or elevation respectively, assessed by a two-way ANOVA (p <0.05). Similarly, neurons were classified \nas tone frequency-selective if they were responsive and presented a significant change in response with tone \nfrequency, assessed using a two-way ANOVA. Only sessions with at least 10 selective boutons were included \nin analysis that treated sessions as units.  \n \nAnalysis of location or frequency tuning was performed on trial-averaged responses. To assess tuning to sound \nazimuth, responses were averaged across stimulus elevations, and the resulting 1-D vector was fit with a 1-\ndimensional Gaussian curve. The peak of this Gaussian was taken as the bouton’s “best azimuth”. Only \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 30 \nboutons with an r2 of Gaussian fit >0.6 were included in analysis of best azimuth. Results were qualitatively \nsimilar when defining best azimuth as the azimuth evoking the highest F/F, without fitting a Gaussian. Azimuth \ntuning width was calculated as the full-width at half-max (FWHM) of the Gaussian fit to the azimuth tuning \ncurve.  \nTo calculate the best elevation for each bouton, responses across azimuths within the bouton’s preferred visual \nfield portion (ipsilateral, centre or contralateral) were averaged. The elevation that evoked the strongest activity \nwas taken as “best elevation”. Best visual azimuth and elevation in VC neurons were obtained by fitting a 2-\ndimensional Gaussian to their 2-dimensional receptive fields. For cases where the r2 of the fit was < 0.3, the \nazimuth and elevation evoking the highest average F/F were taken instead.  \nTo quantify tuning to tone frequency, responses across all sound intensities for each frequency were averaged. \nThe resulting frequency tuning curve was fit with a spline, and the highest peak was taken as “best frequency”. \nFrequency tuning width was calculated as the FWHM of the spline fit. Boutons were classified as “multi-\npeaked” if responses to a frequency at least one octave away from the best frequency were at least 75% as \nstrong as the response to the best frequency. Only non-double peaked boutons were included in best \nfrequency analysis. To calculate the sparsity index (SI) of pure tone responses, the following formula was \nused81:  \n     𝑆𝐼 =  ⎩⎪⎨\n⎪⎧\n1 −\n⎝\n⎜\n⎛൬∑ 𝑟௜\n𝑛 ൰\nଶ\n∑ ቆ 𝑟௜\nଶ\n𝑛 ቇ\n⎠\n⎟\n⎞\n⎭⎪⎬\n⎪⎫\nቀ1 − 1\n𝑛ቁ\n \n          (Equation 8) \nWhere r i denotes the average response of a given bouton to a given stimulus (i.e. a combination of tone \nfrequency and intensity) and n is the number of different stimuli. \nSignal correlations \nPaired signal correlations between axons were calculated as the Pearson correlation between their trial-\naveraged stimulus responses (i.e. their LRAs or FRAs). For signal correlations involving azimuth or elevation \nrepresentations, responses were averaged across the non-relevant dimension in each case. \nGrouping of boutons into axons \nWe used an interactive clustering procedure based on correlations in activity over time to group boutons \nbelonging to the same axon, similar to the procedure described in Mazo et al 29. For each session, a random \npair of boutons with a Pearson correlation above a threshold of 0.4 was selected as the initial seed for a cluster. \nAdditional boutons were then assigned to that cluster if their correlation with any bouton in the cluster exceeded \nthe threshold, or they initiated their own cluster otherwise. For each cluster, the bouton with the highest \naverage F/F was selected to represent that axon. The threshold 0.4 was determined based on empirical \ncorrelations between boutons belonging to the same axon versus different axons. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 31 \nHierarchical bootstrap \nA hierarchical bootstrap approach was used for analysis at the bouton/axon level, as described in refs 82. For \neach bootstrap sample, mice were resampled with replacement, followed by resampling of experimental \nsessions and subsequently boutons/axons. A statistic of interest was then calculated for each bootstrap \nsample. This statistic was either the difference in means (for signal correlations) or the difference in medians \n(for frequency tuning). We then calculated the quantile q of the bootstrap distribution at 0, and calculated the \ntwo-sided bootstrap p-value as 2 × min{q, 1 − q}.  \nLinear mixed models \nLinear mixed models (LMMs) were used for session-level analyses and implemented using the MATLAB \nfunction fitlme. LMMs were primarily used to assess correlations between variables or to test for differences \nacross areas, in combination with a mixed effects ANOVA. In all models, the antero-posterior and medio-lateral \npositions of the injection site in each animal were included as fixed effects, while animal identity was included \nas a random effect. For example, when analysing best sound elevation, the model included an intercept, visual \narea as a categorical fixed effect, injection site coordinates (anteroposterior and mediolateral) as covariates, \nand a random intercept for animal. \nFacial motion analysis \nFacial motion energy was calculated as the frame-to-frame change in luminance for each pixel in the face \nvideo, averaged across all pixels. In some cases, motion energy traces were filtered to remove oscillations \ncaused by laser intensity changes with imaging depth (Butterworth stopband filter centred at 3 Hz). Facial \nprincipal components (PCs) were obtained via singular value decomposition, and projected back on the time \naxis, using the algorithm implemented in the facemap toolbox 83, https://github.com/MouseLand/facemap). \nSome facial motion PCs were dominated with the laser flicker, while others were unaffected. PCs with a strong \n3 Hz spectral component were excluded from further analysis. Locomotion speed was recorded via a rotary \nencoder attached to the wheel axle. All behavioural variables were downsampled to the two-photon acquisition \nrate (6-6.7 Hz) prior to analysis.  \nStimulus decoding from facial motion \nA linear support vector machine (SVM) classifier was used to decode stimulus type (sound azimuth, elevation, \ntone frequency or tone intensity) using the first 100 PCs of facial motion. Motion in each PC was averaged \nover the response period and z-scored before classification. Trials were pooled across non-relevant \ndimensions (e.g. for decoding stimulus azimuth, trials were pooled across elevations). The classifier was \ntrained and tested on held-out data using 10-fold cross validation. For each session, the classifier was run 10 \ntimes, subsampling 50 trials of each stimulus type at random without replacement per iteration. Average \naccuracy across stimulus types was then computed across iterations.  \nOther statistical analysis \nAfter testing for differences in stimulus tuning across areas using the methods described above, pairwise \ncomparisons between areas were performed using the Mann–Whitney U test. To control the false discovery \nrate due to multiple testing, the Benjamini–Hochberg procedure was applied to the resulting p-values.  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 32 \nData availability  \nPreprocessed data required to reproduce the analyses will be deposited and made publicly available prior to \npublication. Both preprocessed and raw data are available from Florencia Iacaruso \n(florencia.iacaruso{at}crick.ac.uk) upon request. \nCode availability  \nThe open-source designs of the speakers and LED device and its associated circuits is available on GitHub \n(https://github.com/Iacaruso-lab/Coliseum). The code for pre-processing and registration of raw MAPseq \ndatasets is available at https://github.com/znamlab/MAPseq_processing. Matlab and python code for \nanalysing the in vivo  data and for reproducing the MAPseq data analyses is available on GitHub \n(https://archive.softwareheritage.org/swh:1:snp:bfb8c2237701bb3d01b702e1c8b49ed17afd3919;origin=https\n://github.com/Iacaruso-lab/AC-VC-paper) .  \nAcknowledgments \nWe thank Johannes Kohl and members of the Neuronal Circuits and Behaviour Lab and the Specification and \nFunction of Neural Circuits Lab for discussions and comments on the manuscript. We thank the Biological \nResearch and Surgical Services Facilities at the Francis Crick Institute for animal care and technical \nassistance, as well as Crick Light Microscopy, Experimental Histopathology, Genomics, Making Lab, \nMechanical Workshop, and Vector Core. This study received support from the Francis Crick Institute, core \nfunding (M.F.I: 10746, CC2118,P.Z.CC2108). The Francis Crick Institute receives its funding and the Francis \nCrick Institute which receives its core funding from Cancer Research UK, the UK Medical Research Council, \nand the Wellcome Trust. The work was also supported by the Engineering and Physical Sciences Research \nCouncil (BBSRC, award ref. EP/X020924/1, M.F.I) \nAuthor contributions \nA.E.W., B.T.B., P.Z. and M.F.I. conceptualised the study. A.E.W., B.T.B. & A.V. performed experiments, \nA.E.W., B.T.B., A.V. & E.M. performed preliminary analysis. A.E.W. & B.T.B. performed formal analysis. P.Z. \n& M.F.I. acquired funding. P.Z. & M.F.I. supervised the project. A.E.W., B.T.B., P.Z. & M.F.I. wrote the paper. \n \nReferences \n1. Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014). \n2. D’Souza, R. D. et al. Hierarchical and nonhierarchical features of the mouse visual cortical network. Nat \nCommun 13, 503 (2022). \n3. Felleman, D. J. & Van Essen, D. C. 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It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 38 \nSupplementary Methods \nOptimisation of second strand synthesis approach for MAPseq library preparation. \nWe tested whether targeted primers for 2nd strand synthesis improve the efficiency compared to RNA template \nfragments. 100ng of spike-in RNA was used for each 20 l reverse transcription reaction. Reverse \ntranscription was performed using SuperScript IV (Thermo Fisher Scientific, #18090050) with the gene-\nspecific reverse transcription primer (Optimization_RT_001) using conditions described previously 30. \nHere, the primer annealing step was carried out at 70 C for 10 min, followed by reverse transcription at \n55 C for 10 mins, followed by enzyme deactivation at 80 oC for 10 min. The resulting first-strand cDNA was \ndivided into two aliquots for either: (a) targeted primer based or (b) RNA template as primer based second \nstrand synthesis. For (a), 0.5 l RNAse H (New England Biolabs, #M0297L) was added to 10 l of 1st strand \nreaction and incubated for 20mins at 37 oC. Second strand synthesis was then carried out by adding 0.75 l \nprimer (Optimization_PCR_002 ), 0.2 l AccuPrime Pfx (Thermo Fisher Scientific, #12344032), 2.5 l 10x \nbuffer, and water to a final volume of 25 l. The sample was heated to 95 C for 2.25 secs, then 68 C for 2 \nmin, and placed on ice. For (b), 2 nd strand synthesis was performed as previously described 30. Per 10 l first-\nstrand cDNA, 6.68 l water, 4.68 l second strand buffer, 0.63 l 10mM dNTP (Thermo Scientific, #10319879), \n0.21 l RNase H (New England Biolabs, #M0297L), 0.84 l DNA Polymerase I (TaKaRa #2130A), 0.21 l E. \ncoli DNA ligase (New England Biolabs #M0205S) were added and incubated at 16 C for 2 hours, then placed \non ice. Subsequently, 0.84 l T4 DNA polymerase (New England Biolabs #M0203S) was added and incubated \nat 16 C for 10 mins, then placed on ice.  \nBoth (a) and (b) second strand synthesis reactions were purified with a 2x bead clean-up using KAPA Pure \nBeads (Roche , #KK8001), washed twice with 80% ethanol, and eluted in 10 l water. 8 l of the second \nstranded cDNA eluate was exonuclease treated by adding 1 l Thermolabile Exonuclease I (New England \nBiolabs, M0568L) and 1 l of buffer r3.1, and incubated in a thermocycler for 60 mins at 37 C followed by 20 \nmin at 80 C. ExoI-treated cDNA was then amplified by nested PCR. The first round of PCR was performed \nfor 12 cycles using AccuPrime Pfx with primers Optimization_PCR_002 and Optimization_PCR_003 and 10 \nl of cDNA. 10 l Thermolabile Exonuclease I was then added to the 50 l PCR reaction, incubated at 37 C \nfor 30 min and heat inactivated at 80 C for 20 min. The second PCR reaction was performed using primers \nOptimization_PCR_004 and Optimization_PCR_005 (see primer table for oligo sequences) for 5 cycles on 7.5 \nl of the PCR product. 20 l of the resulting PCR product was purified using Wizard SV Gel and PCR Clean-\nUp System (Promega, #A9281) and quantified using an Agilent 4200 TapeStation. We compared the PCR \nproduct concentrations generated from cDNA from the original or targeted second strand synthesis \napproaches using a paired t-test. \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 39 \nSupplementary figures \n \n \nSupplementary Figure 1. Optimisation of MAPseq Next Generation Sequencing (NGS) library preparation and \nidentification of template switching events. \na, Summary of different second strand synthesis approaches compared in optimisation of MAPseq sequencing library \npreparation. In the non-targeted approach, the nicked mRNA is used as primers for second strand synthesis, while in the \ntargeted approach, RNA is degraded and a targeted primer anneals to the single stranded DNA and used for second strand \nsynthesis. b, Comparison of non-targeted and targeted second strand synthesis approaches in MAPseq library preparation \nshows greater yield of final PCR product using the targeted approach. N = 4, p=0.017, paired two-tailed t-test. c, Diagram \nillustrating template switching during PCR amplification in MAPseq library preparation. d, 2D histogram showing the unique \nmolecular identifier (UMI) count for neuron barcodes with the same UMI, where 1 stmax is the UMI count for the neuron-\nbarcode/UMI combination that is most abundant, and 2 ndmax is UMI count for the neuron-barcode/UMI combination that is \nsecond most abundant. At 1 stmax/2 ndmax ratios of less than 10 we observe a decrease in entropy, as well as d’, an increase \nin AT content in the UMI sequence, suggesting that shared UMIs with neuron barcodes with similar UMI counts are \nresultant of sequencing artifacts or biases in random nucleotide synthesis during oligo synthesis. Ratios with 1 stmax/2 ndmax \n>10 abundance were therefore considered template switching events and only the most abundant neuron-barcode/UMI \ncombination kept, while below this ratio all reads were discarded. e, UMI count distribution for negative control (olfactory \nbulb) LCM samples in MAPseq dataset. Setting a UMI count threshold of >2 removes 95% of barcodes from negative \ncontrol.  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 40 \n \nSupplementary Figure 2. MAPseq comparison to anterograde tracing datasets, and barcode area assignment \napproaches. a, Cortical flatmaps showing distribution of bulk MAPseq barcode counts across cubelets for individual mice. \nb, Flatmaps of summed projection strength of bulk anterograde GFP tracing for three different experiments from the Allen \nConnectivity Atlas. c, Z-scored bulk projection density for individual Allen Connectivity Atlas anterograde tracing \nexperiments plotted against each other. d, Comparison of mean area-specific normalised projection density between mice \nusing the “homogeneous across cubelet” (where barcodes are assumed to be homogeneously distributed across cubelets) \ne, “homogeneous across area,” in which barcode distribution is assumed to be homogeneous across brain areas, and f, \n“area is main” where barcode counts assigned to brain regions with largest representation within each cubelet (see \nmethods).  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 41 \n \n \nSupplementary Figure 3. Identifying overrepresented co-projection patterns while controlling for intrinsic cubelet biases and \nvariation in labelling efficiency.  a, Heatmap showing mean Spearman correlation coefficient between cortical brain areas in LCM \nsamples across all 3 mice. In addition to biases in observed co-projections based on cubelet structure, axon projection labelling efficiency \nby MAPseq Sindbis virus can vary between neurons. Neurons with lower labelling efficiency would have fewer barcode counts in their \ntarget areas, which would lead to some projection targets being missed due to limits in sensitivity. b, To test whether differences in labelling \nefficiency affected observation of co-projection motifs, we simulated   observed/expected number of co-projections with either equal \nefficiency of labelling (left) or variation in neuron labelling efficiency (right). In the simulation, 100,000 neurons were sampled with p=0.2 \nof targeting each of the 10 areas and given either a constant or variable labelling efficiency. Expected co-projections are the product of \nindividual marginal probabilities (independence assumption). We observed that simulated differences in axon barcode labelling efficiency \ncan lead to artificial over-representation of co-projection motifs. c, To correct for spurious over-represented co-projection motifs that arise \nwhen two target areas fall within the same cubelet, we compared observed data to the same dataset after shuffling neuron barcode \nidentifies within cubelets. However, normalising by the log₂ fold change between observed and shuffled simulations revealed that simple \nrandom shuffling (left) does not correct for the overrepresentation bias due to variable efficiency. In contrast, shuffling using the curveball \nalgorithm (right) which preserves individual neuron labelling efficiencies was able to correct for artificial overrepresentation due to variable \nlabelling efficiencies in the simulation. d, As predicted by simulations, applying this same principle to our MAPseq dataset showed that \ncorrecting for both variable labelling efficiencies and shared areas within cubelets using the curveball shuffle approach (right) decreased \nco-projection motif effect sizes compared to the random shuffle approach (which only corrects for shared areas within cubelets, left). e, \nHeatmaps showing conditional probabilities of A1 neurons targeting cortical co-projection targets ‘Co-projection target’ given neurons \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 42 \nalready target a given cortical area (‘Cortical area’). e, shows actual data conditional probabilities. To determine statistical significance \nfrom chance, we used a shuffle population of 100,000 shuffles, maintaining differences in labelling efficiency and marginal probabilities \n(see Methods). For visualisation, the e' heatmap shows the mean of 100,000 shuffles, and e'’, shows the effect size \n(log2(observed/shuffled conditional probabilities) red and blue circle colours indicate increased and decreased effect sizes respectively, \nshading indicates strength of p-value (as indicated on the colour-bar), black outline indicates p<0.05. P-values are calculated from the \nnull shuffled population distribution after Bonferroni correction. \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 43 \n \nSupplementary Figure 4. Anterior-posterior topographic mapping of A1 inputs to VC is not explained by mere proximity. \na, Relationship between A-P source site position and targeting frequency (same as in Fig. 2g but displaying all visual areas).  \nThe y-axis shows the proportion of neurons targeting the given visual area for neurons in a particular A-P source site position. \nThe x-axis is binned A1 source site A-P position with scale in microns normalised to most posterior VC position. P-values are \ncalculated from logistic regression models including distance and mice as covariates. Fitted lines are predicted values from logistic \nregression model combining all mice together for A-P bin centres with mean distance for each centre as covariate, shaded area \nis 95% confidence limits..  b, Average normalised projection strength to visual cortex of neurons in anterior (n=5) or posterior \n(n=4) auditory cortex, computed from EGFP-injection data from the Allen Connectivity Atlas (Methods). Experiments were \nfrom WT (N=5), Cux2-Cre (N=2), Rorb-Cre (N=1) or Rbp4-Cre (N=1) mice. Data was normalised between 0 and 1 for each \nexperiment before averaging. Circles indicate position of injection centroid, circle size corresponds to injection volume. c, \nNormalised projection strength to each visual area, for anterior and posterior injections. Thin lines correspond to individual \nexperiments, thick lines indicate averages. d, Relationship between normalised projection strength and anterio-posterior \nposition of the injection centroid for each visual area. Marker shape indicates transgenic line.  Fit and p-values obtained \nwith linear regression accounting for distance between injection location and visual areas, and inter-animal variability. P-\nvalues are corrected for multiple comparisons with the Bonferroni method. \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 44 \n \nSupplementary Figure 5. Auditory cortex injections for two-photon imaging and intrinsic imaging. a, Coronal brain \nslice at ~bregma -2.9 mm (left, top right) or bregma -3.15 mm (bottom right), showing jGCaMP7b expression (green) in \nauditory cortex in  an example mouse following stereotaxic injection. AUD: Auditory cortex, VIS: visual cortex. AUDp: \nprimary auditory cortex; AUDv: ventral auditory cortex; AUDd: dorsal auditory cortex; AUDpo: posterior auditory cortex; \nTEa: Temporal association area. MGBv/d: Medial geniculate body, ventral/dorsal portion. b, Top view of jGCaMP7b \nexpression area for each mouse (red areas), aligned to the Allen Brain Atlas. N= 15 animals. Ect: Ectorhinal cortex; SSs: \nSomatosensory supplementary area. c, Percentage of jGCaMP7b expression volume by brain area, in each animal (black) \nand averaged across animals (red). N = 16 animals. Hipp.CA1: Hippocampus, CA1 field. d , Retinotopic mapping using \nintrinsic imaging and area map alignment in an example mouse. Left: Schematic of visual stimulation with drifting gratings. \nMiddle: Example azimuth (top) and elevation (bottom) retinotopic maps obtained with intrinsic signal imaging. Right: \nAlignment of visual area boundaries from Allen Brain Atlas to retinotopic maps, based on retinotopic reversal locations. \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 45 \n \n \nSupplementary Figure 6. Mouse facial movements depend on auditory stimulus characteristics. a, Example frame \nfrom recording of mouse face during sound presentation. Motion energy was extracted from facial motion videos, and \nsingular value decomposition (SVD) was used to obtain principal components of facial motion (Methods). b, Average face \nmotion energy in an example session, aligned to the onset of auditory (red) or visual (blue) stimuli. Lines indicate mean ± \nsem. Shaded gray area indicates time of stimulus presentation. c, Relationship between tone frequency (left) and facial \nmotion energy after stimulus presentation. Thin lines indicate average motion for one animal, thick black line indicates \nmedian across animals. P-value indicates significance of modulation with stimulus frequency (Friedman test). d, Accuracy \nof a support vector machine decoder (SVM) in estimating the octave of a pure tone based on the 100 first PCs of facial \nmotion, using either measured or trial-shuffled data. P-value, Wilcoxon signed-rank test. e-f, Same as c-d, for pure tone \nintensity. g-h, Same as c-d, for sound azimuth. i-j, Same as c-d, for sound elevation. \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 46 \n \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 47 \nSupplementary Figure 7: Subtraction of motor component estimated with generalised linear model minimally \naffects auditory representations in AC boutons. a, Left, schematic of GLM structure. A model using motor-related and \nstimulus-related variables was fit to the neural activity of each bouton. Right, example of an AC bouton activity trace (F, \nblack), reconstructed using the fits from the stimulus-related, motor-related of all variables (F pred,stim, Fpred,motor and Fpred,full, \nrespectively). b, Cross-validated variance explained by motor variables vs stimulus variables for the AC boutons in one \nexample session. Each point corresponds to one bouton. The traces in a correspond to the bouton with a red circle. c, \nPercentage of boutons in each session for which a model containing motor-related, stimulus-related or all variables \nexplained a significant amount of variance across folds (Wilcoxon signed rank test), within the frequencies (top) or locations \n(bottom) dataset. Circles indicate proportion in one session, horizontal bars are median across recordings. N frequencies \ndataset=161 sessions, N locations dataset=135 sessions. d, Cumulative variance explained by stimulus-related, motor-related or \nall variables for all boutons with a significant model of the corresponding type, in the two datasets. Thin lines indicate the \ncumulative variance for boutons in one session, thick lines contain data from all sessions. e, Median variance explained \nby session for each set of variable combinations, in the two datasets. f, Example AC bouton fluorescence trace (Fmeasured), \nnormalised running speed (a.u.), fluorescence trace predicted by stimulus and motor models (F pred,stim and Fpred,motor) and \ntrace after subtracting either the motor model prediction (Fmeas. - Fpred,motor) or the stimulus model prediction (Fmeas. - Fpred,stim). \ng, Example locomotion tuning curve of one AC bouton, computed from measured traces or from traces after subtracting \npredictions from motor variables (green) or stim variables (purple). Running was grouped into 15 equally spaced bins, \npoints indicate the normalised avg. fluorescence at time points when locomotion speed was in a given bin. Error-bars \nindicate s.e.m. h, Percentage of boutons tuned to running speed or face movement (average percentage across face \nmotion PCs) in each dataset, computed either from measured traces or from traces after subtraction of predictions from \nmotor or stimulus variables. Points indicate median across recordings, error bars indicate 25 th and 75 th percentiles. i, \nFrequency-response areas (FRAs) of example AC bouton, computed from measured trace or from trace after subtracting \nmotor or stimulus predictions. j, Percentage of tone responsive and frequency-selective AC boutons, computed from \nmeasured or motor-subtracted traces. Circles indicate percentage in one session, thick bars indicate median across \nsessions. k, Signal correlation between FRAs computed from measured or motor-subtracted traces. l, Distribution of best \nfrequencies, calculated from measured or motor-subtracted traces. P-value from Wilcoxon signed rank test. m, Absolute \ndifference (Δ) in best frequency calculated from measured or motor-subtracted traces. n, Location-response areas (LRAs) \nof example AC bouton, computed from measured trace or from trace after subtracting motor or stimulus predictions. o, \nPercentage of bandpass-noise responsive, sound-azimuth-selective or sound-elevation-selective AC boutons, computed \nfrom measured or motor-subtracted traces. p, Signal correlation between FRAs computed from measured or motor-\nsubtracted traces. q, Distribution of best sound azimuth, calculated from measured or motor-subtracted traces. P value \nobtained with Kolmogorov-Smirnov test. r, Absolute difference (Δ) in best sounds azimuth calculated from measured or \nmotor-subtracted traces. P values obtained with Wilcoxon signed-rank test expect where indicated otherwise \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 48 \n \n \nSupplementary Figure 8. Comparison of tone frequency representations in AC →VC boutons across areas. \na, Percentage of tone-responsive AC boutons in each recording. Black bar indicates median across recordings. \nb, Percentage of tone-responsive boutons that were selective for tone frequency or tone intensity in each recording. \nc, Distribution of best frequency for all single-peaked frequency-selective boutons. d, Distribution of best frequency for AC \nboutons innervating each visual area (filled distributions) or all areas combined (black line). e, Pair-wise comparison of \nmedian best frequency between visual areas. P-values obtained with hierarhical bootstrap.   f, Median tuning width of \nfrequency-selective boutons in each recording, grouped by area. Bar indicates median across recordings. gray P-value \nindicates comparison across all areas (mixed effects ANOVA), black p-value indicates comparison between area pairs \n(Mann Whitney U test). g, Median sparsity index (Methods) of frequency-selective boutons in each recording. h, \nPercentage of multi-peaked FRAs in each recording, grouped by visual area. See Fig. 3e for examples of single-peaked \nand multi-peaked FRAs. i, jGCaMP7b expression area for each mouse used for frequency-tuning experiments (gray \nareas), aligned to the Allen Brain Atlas. Black dots indicate injection centre positions. N = 15 animals. j, Relationship \nbetween anterio-posterior (left) or dorso-ventral (right) position of the injection centroid  and best frequencies measured in \naxonal projections to visual cortex. Each point corresponds to one session. Fit and p-value obtained with linear regression \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 49 \n \nSupplementary Figure 9. Additional analysis of sound location tuning in AC →VC boutons. a, Matrix of average \npairwise signal correlations in azimuth (left) or elevation (right) tuning curves between AC boutons, grouped by visual area. \nb, Left, matrix of average pairwise signal correlations between location response areas (LRAs) of AC axons (i.e. after \ngrouping boutons into axons based on activity correlations, see Methods). Right, signal correlation between axons of the \nsame or different streams. c, Width of the azimuth tuning curve (full-width at half-max) for all azimuth selective boutons, \ngrouped into ipsi-, centre-or contra-lateral tuned based on the peak of their tuning curve. d, Percentage of ipsi-lateral tuned \n(left) or contra-lateral tuned (right) boutons per recording, grouped by visual area and visual stream. P-values obtained \nwith mixed-effects ANOVA. e, Correlation between percentage of centre-tuned V1 neurons and percentage of centre-tuned \nAC→V1 boutons in each spatial bin. R and p value obtained with Spearman correlation. f, Correlation between best visual \nlocation in VC neurons and best sound location in AC boutons in each spatial bin, for all VC (left) or within V1 (right).   \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint \n\n 50 \nSupplementary Figure 10. Effect of injection location on observed location tuning in AC →VC boutons. a, \njGCaMP7b expression area for each mouse used for location-tuning experiments grouped into anterior (> 600 µm, N = 5) \nand posterior (< 600 µm, N =8) mice. Dots indicate injection centroids. b, Relationship between injection position in the \nantero-posterior axis and percentage of centre-tuned AC boutons. Each point corresponds to one session. R and p-value \nobtained with Spearman correlation. c, Distribution of best sound azimuth for boutons from animals with anterior (N= 2250 \nboutons) or posterior injections (N= 6601). d, Percentage of centre-tuned boutons per 300 µm bin in aligned cortical map, \nfor animals with anterior or posterior injection location. e, Relationship between injection position in the antero-posterior \naxis and best sound elevation. Fit and p-value from linear regression. f, Average best elevation of AC boutons for each \nsession, grouped by injection location. Lines indicate median across sessions. g, Best sound elevation per 300 µm bin on \naligned cortical map for animals with anterior or posterior injection location. h-n, Same as a-g, comparing animals with \nventral (< 400 µm, N =6) or dorsal (> 400 µm, N=7) injection sites. N ventral, azimuth dist.: 3075 boutons; N dorsal, azimuth dist.: 5776 \nboutons. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.29.667237doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}