{"paper_id":"250d9661-018f-4c2c-badd-e8daa96352d1","body_text":"1 \n \nBackground optic flow modulates responses of multiple descending interneurons to object 1 \nmotion in locusts 2 \n 3 \nShort title: Optic flow modulates descending interneuron responses to object motion in locusts 4 \n 5 \nSinan Zhang 1*,# and John R. Gray 1  6 \n 7 \n1 Department of Biology, College of Arts and Science, University of Saskatchewan, Saskatoon, 8 \nSaskatchewan, Canada 9 \n# Current address: Department of Medical Genetics, Cumming School of Medicine, University of 10 \nCalgary, Calgary, Alberta, Canada 11 \n 12 \n* Corresponding author 13 \nE-mail: sinan.zhang@ucalgary.ca (SZ) 14 \n 15 \n 16 \n 17 \n 18 \n 19 \n 20 \n 21 \n 22 \n  23 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n2 \n \nAbstract 24 \nAnimals flying within natural environments are constantly challenged with complex visual 25 \ninformation. Therefore, it is necessary to understand the impact of the visual background on the 26 \nmotion detection system. Locusts possess a well-identified looming detection pathway, 27 \ncompromised of the lobula giant movement detector (LGMD) and the descending contralateral 28 \nmovement detector (DCMD). The LGMD/DCMD pathway responds preferably to objects on a 29 \ncollision course, and the response of this pathway is affected by the background complexity. 30 \nHowever, multiple other neurons are also responsive to looming stimuli. In this study, we 31 \npresented looming stimuli against different visual backgrounds to a rigidly-tethered locust, and 32 \nsimultaneously recorded the neural activity with a multichannel electrode. We found that the 33 \nnumber of discriminated units that responded to looms was not affected by the visual 34 \nbackground. However, the peak times of these units were delayed, and the rise phase was 35 \nshortened in the presence of a flow field background. Dynamic factor analysis (DFA) revealed 36 \nthat fewer types of common trends were present among the units responding to looming stimuli 37 \nagainst the flow field background, and the response begin time was delayed among the common 38 \ntrends as well. These results suggest that background complexity affects the response of multiple 39 \nmotion-sensitive neurons, yet the animal is still capable of responding to potentially hazardous 40 \nvisual stimuli.  41 \n 42 \nKeywords 43 \nmotion detection, optic flow, population coding, Locusta migratoria 44 \n 45 \n 46 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n3 \n \nIntroduction 47 \nSuccessful navigation in the natural environment requires animals to rapidly perceive and extract 48 \nimportant sensory cues, usually from a complex, “noisy” background. For example, self-motion 49 \nof animals generates optic flow, which is defined as the pattern of image shifts caused by the 50 \nmotion of the observer [1]. Although animals can use optic flow to control locomotion speed and 51 \ncorrect for deviations in flight orientation [2–5], the presence of optic flow also makes it more 52 \ndifficult to observe other moving objects, such as an approaching predator. Successful detection 53 \nand discrimination between different types of visual motion is critical to animal’s survival.  54 \nIn mouse retina, four subtypes of direction-sensitive ganglion cell (DSGC) are involved in the 55 \nencoding of optic flow [6]. Each subtype of DSGC is selectively sensitive to optic flow along a 56 \nspecific translatory orientation, including forward, backward, up, and down. Combination of 57 \nthese subtypes can also encode rotatory degrees of freedom. Flying animals, however, 58 \nmanoeuvre in additional degrees of freedom, including but not limited to vertical translation and 59 \nroll, neither of which is commonly executed by land animals. Therefore, flying animals need to 60 \nprocess more complicated optic flow parameters. With a relatively tractable nervous system and 61 \ncapability to perform complex behaviours, insects are ideal subjects to study the neural coding of 62 \ncomplex visual backgrounds. In flies, similar to mice, four types of direction-sensitive neurons 63 \nare involved in the computation of the optic flow direction [7].  64 \nThe migratory locust, Locusta migratoria, is another classic subject for studying visual 65 \nprocessing. Migratory locusts have two morphological states: the solitary state and the 66 \ngregarious state. In the gregarious state, locusts can form large swarms, comprised of thousands 67 \nof individuals, and fly ~3 meters·s-1 [8]. With conspecifics moving around at various velocities 68 \nfrom different angles, along with the flow field generated from self-motion, locusts flying in a 69 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n4 \n \nswarm can still avoid collision with conspecifics [9] and predatory birds [10]. In locusts, an 70 \nestablished neural pathway, including the lobula giant movement detector (LGMD) and its 71 \npostsynaptic partner, the descending contralateral movement detector (DCMD), has been 72 \nidentified to respond preferably to objects on a collision course [11–14].  73 \nThe LGMD/DCMD pathway is considered to play an important role in generating collision 74 \navoidance behaviours. Each LGMD receives input from an entire compound eye [15]. When 75 \npresented with an approaching object, the LGMD responds with increased firing rate, which 76 \npeaks near the projected time of collision (TOC) and then decays [16–21]. Each LGMD spike 77 \ngenerates a spike in the DCMD [22], which synapses with the motor center related to avoidance 78 \nbehaviours [23]. Different phases of responses recorded in the LGMD/DCMD pathway have 79 \nbeen associated with critical timing of avoidance behaviours, such as jumping [24] and flight 80 \nsteering [25].   81 \nAddition of visual background affects the response of the LGMD/DCMD pathway to looming 82 \nobjects. In the LGMD, the number of spikes evoked by small looming objects is inhibited by the 83 \npresence of large-field optic flow [13,26,27]. In the DCMD, although the general peak response 84 \nis largely invariant between background types, flow field still caused a lower peak firing rate, 85 \ndelayed peak time, shorter rise phase, and longer fall phase [28].  86 \nPreviously, we investigated putative population coding of motion sensitive neurons in response 87 \nto objects moving against a simple white background [29]. To further understand how motion 88 \nsensitive neural ensembles behave in a more natural environment, we presented a looming 89 \nstimulus approaching against different backgrounds to rigidly-tether locust and recorded the 90 \nsimultaneous activity of multiple descending interneurons. Overall, most discriminated units 91 \nresponded to looming stimuli regardless of background types, but the distribution of different 92 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n5 \n \ntypes of response was affected. For individual units that peaked near TOC, as well as common 93 \ntrends that peaked near TOC, the peak time and/or the response begin time was delayed by the 94 \nflow field background.  95 \n 96 \n  97 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n6 \n \nMaterials and Methods 98 \nAnimals 99 \nTwenty-one adult male locusts (Locusta migratoria) were chosen from a colony maintained at 100 \nthe University of Saskatchewan, Saskatoon, Canada, ensuring that they were at least 3 weeks 101 \npast the imaginal moult. These locusts were fed with a diet consisting of wheat grass and bran 102 \nand were subjected to a regular light-dark cycle of 12 hours of light and 12 hours of darkness at 103 \nan approximate temperature of 30 ° C. To maintain the gregarious state, the locust colony was 104 \nkept in a crowded condition [30]. 105 \n 106 \nPreparation 107 \nDuring the light cycle, the locusts were carefully removed from the colony and transferred to a 108 \nwire mesh container. To provide both light and warmth, an incandescent lamp was placed on top 109 \nof the container. The experiments were conducted at a controlled room temperature of 110 \napproximately 25 ° C. 111 \nTo prepare the locust for the experiments, the legs were first removed, and a rigid tether was then 112 \nsecurely attached to the ventral surface of the thorax using melted beeswax. A small portion of 113 \nthe ventral cervical cuticle was removed to expose the paired connectives of the ventral nerve 114 \nanterior to the prothoracic ganglion. The prepared locust was then transferred to the stimulus 115 \narena [31] (Fig 1.A). A silver wire hook electrode was used to lift and stabilize the left ventral 116 \nnerve cord. Subsequently, a sharp glass electrode was carefully inserted into the protective 117 \nsheath surrounding the left nerve cord, creating a small opening. A twisted wire tetrode, 118 \nfabricated following an established protocol [32], was then inserted into the left nerve cord 119 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n7 \n \nanterior to the hook electrode (Fig 1.B). Finally, a silver ground wire was inserted into the 120 \nabdomen.  121 \n 122 \nFig 1. Experimental setup and visual stimuli.  123 \n(A) The stimulus arena with the dome screen closed. (B) The locust preparation with tetrode 124 \ninserted (inset). (C) Diagram of the experimental setup. Visual stimuli were rendered and 125 \nprojected onto the dome screen while neural activity was recorded simultaneously. A 5-V pulse 126 \nwas used to align the neural recordings with stimulus timing. (D) Visual stimuli used in the 127 \nexperiment. The first three visual stimuli included a 7-cm-diameter black disc approaching at 128 \n300 cm· s-1 from the bottom right at 45°  angle, against three visual backgrounds: white (LW), 129 \nhalf-white and half-grey (LG), and flow field (LF). The last visual stimulus was composed of 130 \nflow field only (F). Each visual stimulus was presented 5 times and the order of all 20 visual 131 \nstimuli was randomized for each locust. 132 \n 133 \nNeural responses were observed while waving a hand to verify a high signal-to-noise ratio. A 134 \nmixture of mineral oil and Vaseline was then applied around the electrodes and nerve cords to 135 \ninsulate the recordings from the hemolymph and prevent desiccation. The entire setup was 136 \nrotated 180° , placing the locust in a dorsal-side-up position at the center of the rear projection 137 \ndome screen (Fig 1.C). The longitudinal axis of the locust was aligned parallel to the apex of the 138 \ndome, with a distance of 12 cm between the locust's eye and the screen. In this orientation, 0° 139 \nrepresented the front of the locust, 180°  represented the direction directly behind, and +90°  140 \nrepresented a perpendicular angle to the right longitudinal axis of the locust's body. Negative 141 \nperpendicular angles were to the left of the locust. 142 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n8 \n \n 143 \nVisual Stimulus 144 \nVisual stimuli were generated using a Python-based program EVSG [33]and incorporated 145 \ncorrectional factors to account for the curvature of the dome screen. The resolution of the stimuli 146 \nwas set at 1024× 768 pixels. Each pixel on the screen corresponded to approximately 0.7 mm, 147 \nresulting in a subtense angle of approximately 0.4° , which is well below the angular resolution of 148 \nlocusts' eyes (1° ) [34]. 149 \nReal-time rendering of the images was performed using a GeForce RTX 3080 video card 150 \n(NVIDIA Corporation, Santa Clara, United States), achieving a frame rate of over 150 frames 151 \nper second. The images were then projected onto the dome screen using an InFocus DepthQ 152 \nprojector (InFocus, Portland, United States) with the colour wheel removed. The frame rate of 153 \nthe projections exceeded the flicker fusion frequency of locusts' eyes, which is around 66 Hz 154 \n[35]. 155 \nA 5V pulse was generated at the projected time of collision (TOC), which was automatically 156 \ncalculated based on the motion trajectory of the visual stimuli. This pulse was used to align 157 \ndifferent trials.  158 \nIn all visual stimuli, the backgrounds were divided vertically into two equal halves, simulating a 159 \nhorizon line positioned at 0° elevation. The top half was rendered in solid white (R = 0, G = 0, B 160 \n= 0), while the bottom half was either solid white (LW), solid grey (LG, R = 100, G = 100, B = 161 \n100), or a flow field (LF), composed of grey concentric circles (R = 100, G = 100, B = 100) 162 \nexpanding outward from the center of the dome below the horizon line. The Michelson contrast 163 \nratio between the grey and the white backgrounds was 0.87. (Fig 1.D). Against each background, 164 \na 7-cm diameter black disc (R=255, G = 255, B = 255) approached the locust at a velocity of 300 165 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n9 \n \ncm·s-1 (looming) from the bottom right at 45° azimuth and -45° elevation.The trajectory of the 166 \nlooming disc was designed so that it remained within the lower half of the dome screen for the 167 \nmajority of its motion. Additionally, a fourth visual stimulus (F) consisted of only the flow field 168 \nbelow the horizon line.  169 \nEach visual stimulus was presented 5 times, resulting in a total of 20 presentations, the order of 170 \nwhich was randomized for each locust. To prevent neural habituation, the visual stimuli were 171 \npresented with a 3-minute interval between each presentation. Additionally, a direct loom was 172 \npresented before and after the sequence of the 20 presentations to ensure that no attenuation of 173 \nthe neural responses occurred during the length of the experiment.  174 \n 175 \nData Acquisition 176 \nThe neural signals from all 5 channels, including 1 channel from the silver hook electrode and 4 177 \nchannels from the twisted wire tetrode, were amplified using a differential amplifier (Model 178 \n1700, A-M Systems, Sequim, United States) with a high pass filter at 300 Hz, a low pass filter at 179 \n5 kHz, and a gain of 100× . These amplified neural signals, along with the stimulus pulse, were 180 \nthen digitized using a USB data acquisition board (DT9818-OEM, TechmaTron Instrument Inc., 181 \nLaval, Canada) and recorded at a sampling rate of 25 kHz per channel using DataView version 182 \n11 (W.J. Heitler, University of St Andrews, St Andrews, Scotland). 183 \n 184 \nSpike Sorting 185 \nRaw recordings were merged chronologically into a single file and then filtered using a Finite 186 \nImpulse Response (FIR) filter with a bandpass range of 500-2000 Hz and a Blackman window 187 \ntype using DataView. The filtered data from the four channels of the tetrode was exported to 188 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n10 \n \nOffline Sorter version 4.6 (Plexon Inc., Dallas, United States) for waveform detection and spike 189 \nsorting. The waveform detection threshold was set to three times the standard deviation over the 190 \nmean of each channel, and the detected waveforms were aligned to the largest peak on either 191 \nside. Spike sorting was performed using a semi-automatic method based on the T-Dist E-M 192 \nalgorithm [36]. The degree-of-freedom (DOF) multiplier was set to 3, and the initial sorting was 193 \nperformed with 26 units in the 3D feature space. Subsequently, manual discrimination was based 194 \non the shape and amplitude of the waveforms for final unit sorting. A Multivariate Analysis of 195 \nVariance (MANOVA) was used to assess the statistical distinctness of the sorted units in the 3D 196 \ncluster space (Fig 2.B).  197 \n 198 \nFig 2. Spike sorting and identification of responsive units.  199 \n(A) Raw recording from the hook electrode, the stimulus event pulse (stim), and the tetrode are 200 \nshown on the top. Waveforms were detected using average plus three times the standard 201 \ndeviation as the threshold, and sorted based on the cluster variance in the 3D feature space. Spike 202 \nsorting results from the same recording time window (tetrode sorted) is shown underneath the 203 \nraw recordings. Each colour represents a different unit. (B) Overlapped waveforms of three units 204 \nrecorded from all four channels of the tetrode (top), and the distribution of the same units in the 205 \n3D feature space (bottom). (C) Identification of responsive units. For each discriminated unit, the 206 \nperistimulus time histogram (PSTH), representing the change of firing rate during the stimulus 207 \npresentation, is shown on the bottom of each block. The cumulative sum and 99% confidence 208 \nlevel (represented by the ellipses) are plotted on the top half of each block. If the cumulative sum 209 \n(represented by the line inside the ellipse) extended outside the ellipse (example shown in the 210 \norange shade), this unit was determined to have responsed to the specific visual stimulus. If the 211 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n11 \n \ncumulative sum remained within the ellipse for the entire duration (example shown in the blue 212 \nshade), this unit was determined to have not nonresponded. Note that not all units generated 213 \nspikes during this stimulus presentation. Inactive and non-responsive units were removed from 214 \nsubsequent analyses.  215 \n 216 \nSpike Train Analysis 217 \nThe spike times of discriminated units were exported to NeuroExplorer Version 5 (Plexon, 218 \nDallas, United States) for further analysis. Peristimulus time histograms (PSTHs) were generated 219 \nusing a 1-ms bin width and smoothed with a 50-ms Gaussian filter. All trials were aligned to the 220 \nprojected time of collision (TOC). For trials involving a looming disc, a 2.2-second window was 221 \nused, starting 2 seconds before TOC, and ending 0.2 seconds after TOC. For trials with the flow 222 \nfield only, a 10-second window was employed, spanning 5 seconds before the flow field onset 223 \nand ending 5 seconds after.  224 \nTo evaluate the responsiveness of individual units, cumulative sum plots were created along with 225 \nan ellipse representing the 99% confidence level (Fig 2.C). The cumulative sum was calculated 226 \nin NeuroExplorer using the following algorithm: 227 \nFor bin 1: cs(1) = bc(1) - A; 228 \nFor bin 2: cs(2) = bc(1) + bc(2) - A× 2; 229 \nFor bin 3: cs(3) = bc(1) + bc(2) + bc(3) - A× 3; 230 \n... 231 \nIn this algorithm, cs(n) represents the cumulative sum of the n-th bin, bc(n) represents the spike 232 \nbin count of the n-th bin, and A represents the average of all bin counts. If the cumulative sum 233 \nremained within the 99% confidence level ellipse, it indicated that the firing rate of the 234 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n12 \n \ncorresponding unit did not exhibit a significant change during the stimulus presentation, 235 \nimplying that the unit did not respond to the visual stimulus. Conversely, if the cumulative sum 236 \ntouched or extended beyond the ellipse, it was considered responsive to the visual stimuli [37–237 \n39]. Units that did not exhibit a response were excluded from subsequent analyses.  238 \n 239 \nDimensionality Reduction 240 \nThe responses of individual units were first examined to determine how many types of visual 241 \nstimuli that each unit responded to. Between different animals, the same neuron could have been 242 \nrecorded multiple times. Therefore, we pooled the responsive units across all animals based on 243 \nthe visual stimulus. For each stimulus, the response types were initially categorized by observing 244 \nthe peristimulus time histogram (PSTHs) of individual units. The categorization was adopted 245 \nfrom previous studies [37,38]. Firing parameters, including peak firing rate, peak time, rise phase 246 \n(from the time when the unit firing rate exceeded the upper 95% confidence interval to the peak 247 \ntime), and decay phase (from the peak time to when the firing rate decayed below 15% of the 248 \npeak firing rate) [20,40], were measured from the PSTHs of those units that displayed a clear 249 \npeak, and compared between different backgrounds.  250 \nDynamic Factor Analysis (DFA) was then used to extract common trends from the responsive 251 \nunits for each visual stimulus [37,41]. In this model, a set of n observed time series (y) (units) is 252 \nexplained by a set of m hidden random walks (x) (common trends) through linear combination, 253 \nrepresented by factor loadings (Z) and offsets (a). The model equations can be written as 254 \nfollows:  255 \n 256 \nyt = Z × xt + a + vt, where vt ~ MVN (0, R) 257 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n13 \n \nxt = x(t-1) + wt, where wt ~ MVN (0, Q) 258 \n 259 \nIn the model, the error terms of hidden processes (x) (common trends) and observations (y) 260 \n(units) were represented by wt and vt, respectively. Both wt and vt follow a multivariate normal 261 \ndistribution (MVN), with a mean of 0 and covariate metrices of R and Q, respectively. To 262 \nidentify the model, certain constraints were applied: the first m elements of a were set to 0, zi,j 263 \nwas set to 0 if j > i for the first m-1 rows of Z, and Q was set to the identity matrix Im.  264 \nDFA was performed using the MARSS package v3.11.4 [42] in R 4.1.3 (R Core Team, 2022), 265 \nusing the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. The model quality was 266 \nevaluated using the Akaike Information Criterion with a correction for small sample size (AICc) 267 \n[43]. For each visual stimulus, DFA was iteratively performed, starting with one common trend. 268 \nThe number of common trends gradually increased until AICc began to increase, and the model 269 \nwith the lowest AICc was selected as the optimal model. 270 \nThe extracted common trends were further analyzed. The factor loadings (Z) of each common 271 \ntrend were used to rectify any potentially reversed trends and scale the common trends. 272 \nAccording to the DFA model, yt = Z × xt + a + vt, for the i-th common trend ((xT)i) and the 273 \ncorresponding factor loadings (Zi), if (xT)i was multiplied by a scale factor, while Zi was divided 274 \nby the same scale factor, the overall model is not affected. The sign of the scale factor was 275 \ndetermined by the average of Zi, while the value was determined by the largest absolute value 276 \namong all elements within Zi. If the average of Zi was negative, it indicates that the common 277 \ntrend contributes negatively to most unit responses (observations), and therefore reversing the 278 \nsign would make the common trend consistent with the majority of units. The value was chosen 279 \nto scale all factor loadings to (-1, 1), and therefore, to make the scale of all common trends 280 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n14 \n \ncomparable to each other. For common trends showing clear peaks, various parameters, such as 281 \npeak time, rise phase, and decay phase, were measured from the PSTHs. These parameters were 282 \nthen compared between different backgrounds.  283 \n 284 \nStatistical Analysis 285 \nStatistical analyses were executed using SigmaPlot version 12.5 (Systat Software, San Jose, 286 \nUSA) or R 4.1.3 (R Core Team, 2022), and plotted using SigmaPlot. Prior to applying specific 287 \ntests, datasets were preliminarily assessed for normal distribution (Shapiro-Wilk test) and 288 \nhomoscedasticity. Parametric data was articulated through arithmetic mean and standard 289 \ndeviation, and plotted with a column graph. Nonparametric data was articulated through median 290 \nand quartiles, and plotted with a box plot. Variables with no inherent relationship were compared 291 \nthrough either one-way Analysis of Variance (ANOVA) (for parametric data) or Kruskal-Wallis 292 \nOne Way Analysis of Variance on Ranks (for nonparametric data). Variables with an inherent 293 \nrelationship were compared using either one-way repeated measure (RM) ANOVA (for 294 \nparametric data) or Friedman Repeated Measures Analysis of Variance on Ranks (for 295 \nnonparametric data). Two-way comparisons were executed utilizing two-way RM ANOVA and 296 \nHolm-Sidak post-hoc test. All statistical examinations were two-tailed, and the significance level 297 \n(α) was determined to be 0.05.  298 \n 299 \n  300 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n15 \n \nResults 301 \nUnit discrimination 302 \nAll twenty trials from each animal were chronologically merged for spike discrimination and 303 \nunit sorting. Utilizing a positive threshold of three times the standard deviation above the mean, 304 \nwe detected 571,515 spikes across all twenty-one animals (median = 25,637 spikes per locust, 305 \n1st quartile = 23,692 spikes per locust, 3rd quartile = 29,090.5 spikes per locust). From these 306 \nspikes we discriminated 352 distinct units (mean = 16.8 units per locust, standard deviation = 3.4 307 \nunits per locust). Multivariate Analysis of Variance (MANOVA) verified the statistical 308 \ndistinctiveness of these units in three-dimensional cluster space (p < 0.001). A comprehensive 309 \nsummary of the discriminated spikes and units for all animals is provided in S1 Table. 310 \n 311 \nUnit responses 312 \nS2 Table summarizes the number of units that responded to each stimulus type. Out of the total 313 \n352 discriminated units, 248 units (70%) exhibited responses to looming stimuli against a white 314 \nbackground (LW), 238 units (68%) responded to looming stimuli against a half-white-half-grey 315 \nbackground (LG), 242 units (69%) responded to looming stimuli against a flow field background 316 \n(LF), 222 units (63%) responded to the flow field only (F), and 28 units (8%) did not respond to 317 \nany of the visual stimuli. One-way ANOVA revealed that there was no significant difference in 318 \nthe number of units responding to looming stimuli against different backgrounds (F2 = 0.14, p = 319 \n0.87), nor was there a significant difference in the percentage of responsive units from each 320 \nanimal (One-way ANOVA, F2 = 0.49, p = 0.61) (Fig 3).  321 \n 322 \nFig 3. Number of units that responded to each type of visual stimulus. 323 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n16 \n \nWithin each box, the horizontal line represents the median, while the lower and upper 324 \nboundaries, the whiskers, and the dots represent the 25/75%, 10/90%, and 5/95% percentiles, 325 \nrespectively. One-way repeated measure (RM) ANOVA revealed that there was no statistical 326 \ndifference between the number of units that responded to different types of visual stimuli. 327 \n 328 \nHowever, it is worth noting that the responses of these units were not exclusive to a single 329 \nbackground. A unit could respond to looming stimuli against one or more visual backgrounds. 330 \nFig 4 illustrates the distribution of the percentage of different types of looming stimuli that the 331 \nunits responded to. Among the total 352 units, 194 units (55%) responded to looming stimuli 332 \nagainst all three backgrounds. 42 units (12%) responded to two out of the three looming stimuli, 333 \namong which 19 units responded to LW & LG, 15 units responded to LW & LF, and 8 units 334 \nresponded to LG & LF. 63 units (15%) responded to only one type of looming stimulus, among 335 \nwhich 20 units responded to LW, 18 units responded to LG, and 25 units responded to LF. 336 \nAdditionally, 25 units responded exclusively to the flow field (F) and none of the looming 337 \nstimuli.  338 \nOverall, the number of units responding to looming stimuli was relatively similar across all three 339 \nbackgrounds, suggesting that the visual background did not affect the number of units 340 \nresponding within a putative population.  341 \n 342 \nFig 4. Percentage of responsive units based on stimulus properties. Each column represents 343 \nthe number of units (n) that responded to 1, 2, 3 looming stimuli, flow field only, or none. Out of 344 \nall 352 discriminated units, 194 units responded to looming stimuli against all three backgrounds 345 \n(55%), 42 units responded to looming stimuli against two out of three backgrounds (12%), while 346 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n17 \n \n63 units responded to looming against one background (18%). Overall, the background type did 347 \nnot affect how many units responded to the looming stimulus.  348 \n 349 \nCategorization of unit response 350 \nThe peristimulus time histograms (PSTHs) of individual units were examined visually and 351 \ncategorized based on the trend of firing rate change over time for visual stimuli containing a 352 \nlooming stimulus [38]. Five categories of responses were identified (Fig 5): 1) firing rate peaked 353 \nnear the projected time of collision (TOC); 2) firing rate showed a valley near TOC; 3) firing rate 354 \ngradually increased over time; 4) firing rate remained relatively constant; 5) firing rate remained 355 \nconstant during the looming and increased near the end of the recording. S3 Table provides a 356 \nsummary of the distribution of response categories for looming against different visual 357 \nbackgrounds. The distribution of response categories was similar between LW and LG (Chi-358 \nsquare test, χ2 (4, N = 486) = 1.615, p = 0.806). However, the distribution was significantly 359 \naffected by the flow field (LW vs LF: Chi-square test, χ2 (4, N = 490) = 12.541, p = 0.014; LG vs 360 \nLF: Chi-square test, χ2 (4, N = 480) = 19.672, p < 0.001). When presented with LF, only 5 units, 361 \ncompared to 21 for LW and 27 for LG, displayed a gradual increase (category 3). Additionally, 362 \nmore units showed a constant firing rate that increased near the end (category 5). It is likely that 363 \nsome units in category 1 and 3 exhibited a delayed response to LF, leading to their placement in 364 \ncategory 5. Fig 5 illustrates the average PSTH and 95% confidence interval for each category in 365 \nresponse to LW.  366 \n 367 \nFig 5. Categorization of units responding a looming stimulus against white background 368 \n(LW).  369 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n18 \n \nAll peristimulus time histograms (PSTHs), represented by the grey lines, were aligned to the 370 \nprojected time of collision (TOC), represented by the red vertical line. The black solid lines 371 \nrepresent the average PSTH, while the black dotted lines represent the 95% confidence intervals. 372 \nCategory 1 included units that showed a peak near TOC. Category 2 included units that showed a 373 \nvalley near TOC. Category 3 included units that showed a gradual increase during the stimulus 374 \npresentation. Category 4 included units that showed a tonic firing during the stimulus 375 \npresentation. Category 5 included units that showed an increase towards the end of the displayed 376 \nwindow, after TOC.  377 \n 378 \nFor response category 1, which peaked near TOC, various parameters were calculated, including 379 \nthe peak firing rate, peak time (relative to TOC), rise phase, and decay phase [20,38,40] (Fig 6). 380 \nThe rise phase was defined as the time from when the unit's firing rate last exceeded the upper 381 \n95% confidence interval to the peak time, while the decay phase was measured from the peak 382 \ntime to when the firing rate decayed to 15% of the peak firing rate. Among the 194 units that 383 \nresponded to looming against all three backgrounds, 135 units (70%) exhibited a category 1 384 \nresponse to all three looming stimuli. Across different backgrounds, there was significant 385 \ndifferences in both peak time (Friedman Repeated Measures Analysis of Variance on Ranks, χ22 386 \n= 44.236, p < 0.001) and the length of the rise phase (Friedman Repeated Measures Analysis of 387 \nVariance on Ranks, χ22 = 70.673, p < 0.001). However, no significant differences were found in 388 \npeak firing rate (Friedman Repeated Measures Analysis of Variance on Ranks, χ22 = 4.326, p = 389 \n0.115) or the length of the decay phase (Friedman Repeated Measures Analysis of Variance on 390 \nRanks, χ22 = 3.318, p = 0.190). Tukey post-hoc tests indicated that all pairwise comparisons were 391 \nsignificantly different for peak time (LW vs LG: q = 4.088, p < 0.05; LW vs LF: q = 9.338, p < 392 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n19 \n \n0.05; LG vs LF: q = 5.250, p < 0.05), although the difference in peak time between LW and LG 393 \nwas relatively small. For the rise phase, there was no statistical difference between LW and LG 394 \n(Tukey post-hoc test, q = 2.711, p > 0.05), while both LW and LG showed significantly higher 395 \nvalues than LF (LW vs LF: q = 8.650, p < 0.05; LG vs LF: q = 11.361, p < 0.05). It is important 396 \nto note that, as peak times were delayed in LF, the selected trial length (-2 to 0.2 seconds relative 397 \nto TOC) did not always include a full decay phase, i.e. firing rate did not fall below 15% of the 398 \npeak firing rate by the end of the trial. Consequently, the sample size for the decay phase 399 \ncomparison was smaller than that oof the other parameters, making it more difficult to find 400 \nsignificant difference.  401 \n 402 \nFig 6. Comparison of response parameters of units in Category 1 (peak near TOC) between 403 \nbackground types.  404 \n(A) Sample PSTH of a Category 1 unit in response to a looming stimulus. The red vertical line 405 \nrepresents the time of collision (TOC), while the grey dashed line represents the upper 95% 406 \nconfidence interval of the histogram. The last time the instantaneous firing rate exceeded the 407 \nupper 95% confidence interval (t95) was defined as the beginning of the response, while the time 408 \nwhen the firing rate decayed below 15% of the peak firing rate (t15) was defined as the end of the 409 \nresponse. The duration from t95 to the peak was defined as the rise phase, and the duration from 410 \nthe peak to t15 was defined as the decay phase. (B) Within each box, the horizontal line 411 \nrepresents the median, while the lower and upper boundaries, the whiskers, and the dots 412 \nrepresent the 25/75%, 10/90%, and 5/95% percentiles, respectively. The peak time, peak firing 413 \nrate, rise phase, and decay phase of each PSTH was measured and compared using one-way 414 \nrepeated measure (RM) ANOVA. Different letters above the boxes represent statistically 415 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n20 \n \nsignificant differences. The white and grey backgrounds evoked similar peak times (grey being 416 \nslightly later), while flow field evoked a significant peak delay. The flow field also resulted in 417 \nshortened rise phases. 418 \n 419 \nIn summary, among individual responsive units, LW and LG elicited similar responses, while the 420 \naddition of a flow field background (LF) resulted in delayed peak firing and a shorter rise phase 421 \nfor units in category 1, indicating a delayed and more brief response.  422 \n 423 \nCommon trends 424 \nDynamic factor analysis (DFA) was conducted using the MARSS package in R 4.1.3 to extract 425 \ncommon trends from all responsive units. Initial attempts to perform DFA on Gaussian-426 \nsmoothed 1-ms-binned data resulted in consistent crashes of R due to the model's complexity. 427 \nConsequently, the firing rate was recalculated using a 50-ms bin width without smoothing. 428 \nAlthough temporal resolution was reduced, the 50-ms-binned data captured the dynamic 429 \nmodulation of firing rate while significantly reducing computational complexity by a factor of 430 \nfifty. 431 \nFor each of the 4 visual stimuli, DFA was iteratively performed with an increasing number of 432 \ncommon trends, starting from 1. The Akaike information criterion with a correction for small 433 \nsample sizes (AICc) was used to monitor the performance of each model. When the AICc began 434 \nto increase, the iteration was halted, and the model with the lowest AICc was deemed the optimal 435 \nmodel. S4 Table shows that the optimal models for LW and LG both included 7 common trends. 436 \nThis consistency with the optimal model for looming at 0°  elevation against a white background 437 \n[29] indicates that neither the looming trajectory (45°  upwards) nor the half-white-half-grey 438 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n21 \n \nbackground influenced the level of variability in the locust's response to a black looming 439 \nstimulus. In contrast, despite evoking a similar number of responsive units, the optimal model for 440 \nLF only contained 5 common trends, suggesting less variation among unit responses.  441 \nFig 7 depicts activity of all the extracted common trends in response to each stimulus. In 442 \nresponse to a looming stimulus, irrespective of background types, most common trends exhibited 443 \na positive peak near the time of collision (TOC). Against all three backgrounds, only one 444 \ncommon trend did not show a peak, and instead, continuously increased until the end of the 445 \nstimulus presentation (CT5 for LW, CT7 LG, and CT4 LF). When presented with the flow field 446 \nbackground only (F), without the looming stimulus, the primary type of response was a brief 447 \npeak right after time when the flow field started (Tff), followed by a gradual decrease, 448 \nsuggesting adaptation to the flow field. Despite that the best DFA model contained 6 CTs, the 449 \nfirst three CTs all showed this type of response, while the other CTs maintained at a relatively 450 \nstable level with no clear change near Tff.  451 \n 452 \nFig 7. Common trends (CTs) responses to all four types of visual stimuli.  453 \nIn each column, peristimulus time histograms (PSTHs) of the CTs from a stimulus type were 454 \nplotted, aligned to either the projected time of collision (TOC) or the time when flow field 455 \nstarted displaying (Tff). In DFA models, the order of CTs was randomized, i.e., CT1 from the 456 \nfirst and second column are not necessarily the same. The factor loadings of constituent 457 \ndiscriminated units in each CT are shown next to the PSTH in a heatmap. Within a heatmap, 458 \neach horizontal line represents one discriminated unit. The order of units in the heatmap is also 459 \nconsistent within a column. Irrespective of background types, most CTs responded to a looming 460 \nstimulus with a peak near TOC, similar to a DCMD response. The remaining common trend 461 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n22 \n \nfrom each background (CT5 in the white background, CT7 in the half-white-half-grey 462 \nbackground, and CT4 in the flow field background) continuously increased until the end of 463 \nstimulus presentation. For the last visual stimulus, flow field only, CT 1-3 displayed a peak at 464 \nTff, when the flow field started, while CT 4-6 maintained at a relatively constant level without 465 \nclear change near Tff.  466 \n 467 \nAmong the common trends displaying clear peaks, the peak times occurred before or after TOC 468 \nin response to LW and LG, while in response to LF, only one common trend (CT 3) had a peak 469 \ntime before TOC, and all other common trends had peaks after TOC. The median peak time of 470 \nLF common trends was the highest (i.e., latest) among all three looming stimuli, and the rise 471 \nphase for LF was the shortest. These findings aligned with the peak times observed in category 1 472 \nunit responses, although the comparisons of peak time and rise phase were not statistically 473 \nsignificant (One-way ANOVA; peak time, F2 = 0.507, p = 0.618; rise phase, F2 = 0.798, p = 474 \n0.480) (Fig 8), likely due to small sample size and low statistical power. However, it is worth 475 \nnoting that the response start time (t95), defined as when the unit firing rate exceeded the upper 476 \n95% confidence interval, was significantly influenced by the type of visual background 477 \n(Kruskal-Wallis One-Way Analysis of Variance on Ranks, H2 = 7.096, p = 0.014). The response 478 \nbegan later in response to LF, compared to LG (Dunn's post-hoc test, p < 0.05). Since the firing 479 \nrate did not decay below 15% of the peak firing rate before the end of stimulus presentation in 480 \nsome common trends (e.g., CT 1, 3, and 4), the sample size of t15 and decay phase was too small 481 \n(≤ 3 for each background) to derive meaningful comparison.  482 \n 483 \nFig 8. Comparison of response parameters of common trends (CTs) that peaked near TOC.  484 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n23 \n \n(A) Sample PSTH of a common trend in response to a looming stimulus. The red vertical line 485 \nrepresents the time of collision (TOC), while the grey dashed line represents the upper 95% 486 \nconfidence interval. The last time the instantaneous firing rate exceeded the upper 95% 487 \nconfidence interval (t95) was defined as start of the response. We defined the duration from t95 to 488 \nthe peak as the rise phase. (B) Box plots comparing CT firing parameters. Within each box, the 489 \nhorizontal line represents the median, while the lower and upper boundaries and the whiskers 490 \nrepresent the 25/75% and 10/90% percentiles, respectively. Different letters above boxes 491 \nrepresent statistically significant differences. The peak time, rise phase, and response begin time 492 \nof each PSTH was measured and compared using one-way repeated measure (RM) ANOVA. 493 \nSimilar to Fig 5, flow field background resulted in later peak and shortened rise phase in the 494 \nCTs, but the difference was not statistically significant. The response start, however, was 495 \nsignificantly later in the flow field background, compared to the white and grey background. 496 \n 497 \n  498 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n24 \n \nDiscussion 499 \nDuring flight, optic flow is a main source of spatial information an animal can acquire. 500 \nComponents of the optic flow can be used to calculate the distance and trajectory of nearby 501 \nobjects. This is the first study to investigate the effect of a flow field background on looming 502 \nresponses of multiple interneurons in the locust visual system. We found that multiple units were 503 \nresponsive to a looming disc approaching upwards from bottom right at 45°  azimuth and -45°  504 \nelevation, yet displaying various types of responses. Compared to looming against the white 505 \nbackground (LW) and half-white-half-grey background (LG), looming against the flow field 506 \nbackground (LF) affected the distribution of different response categories among responsive 507 \nunits. Common trends (CTs) were extracted and compared between background types. Both LW 508 \nand LG evoked 7 CTs, the same as loom from 90°  azimuth at 0°  elevation, while LF evoked 5 509 \nCTs, suggesting that flow field background reduced the variability among unit responses. In both 510 \nindividual units and common trends, the flow field background caused the responses to be 511 \ndelayed.  512 \n 513 \nFlow field design 514 \nPrevious studies used flow field backgrounds that consisted of vertical bars that expanded across 515 \nthe entire visual field [28,44], or squares expanding from the center of the screen [19]. The type 516 \nof flow field used here was designed to emulate the natural visual environment when the locust is 517 \nflying. Since flying animals are closer to the ground, compared to the sky, the objects on the 518 \nground would appear faster and more conspicuous. Therefore, the optic flow in the lower half of 519 \nthe visual field is more prominent than that in the top half. By dividing the visual background 520 \nvertically into two halves, and showing flow field in the bottom half only, we presented a 521 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n25 \n \nsimplified bbackground that simulates a horizon line. Preliminary testing indicated that there was 522 \nno effect of a flow field on looming responses when the object approached from 45°  azimuth at 523 \n0°  elevation. For this trajectory, the top half of the object approached within the white 524 \nbackground, which likely attenuated any putative effects of a flow field [28], regardless of flow 525 \nfield contrast or velocity. Therefore, we choose a a looming stimulus that moved upwards from -526 \n45°  elevation to ensure the disc was within the flow field except for final 20.2 ms of each 527 \napproach. When presented with looming stimuli approaching within different areas across the 528 \nreceptive field, some parameters of the DCMD response are different, yet the shapes of the 529 \nPSTH remain largely unaffected [45,46]. We found a similar effect among the other motion 530 \nsensitive units and common trends extracted from these units. The number of responsive units 531 \nand the number of common trends, representing the types of responses among all responsive 532 \nunits, were consistent between looming at 0°  elevation and -45°  elevation.  533 \nLocusts display motion dazzle. When presented with a two-tone looming object, in which the top 534 \nhalf is darker than the background while the bottom half is lighter, the DCMD responds less than 535 \nthe top (dark) half alone [47]. However, the flow field we used here was composed of white and 536 \ngrey circles, both lighter than the black object (Michelson contrast ratio between black and white 537 \n= 0.98, Michelson contrast ratio between black and grey = 0.80). Therefore, the motion dazzle 538 \nwas likely not present in our stimuli  539 \n 540 \nEffect of flow field on collision detection   541 \nWhen an animal moves through its environment, optic flow is generated as a translational or 542 \nrotational pattern that is perceived by the eye [48]. Animals use optic flow to access self-motion 543 \nand stabilize trajectory. For example, honeybees use optic flow to maintain flight height [49], 544 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n26 \n \nwhile bumblebees use optic flow in the frontal visual field to control position and flight speed 545 \n[50,51].  546 \nIn locusts, a flow field consisting of white and grey vertical bars can elicit excitatory 547 \npostsynaptic potentials (EPSPs) in the LGMD, quickly followed by inhibitory postsynaptic 548 \npotentials (IPSPs) [40] via activation of lateral and feed-forward inhibition [13,52]. Therefore, 549 \nLGMD response to small looming objects is inhibited by large-field optic flow, represented by 550 \nfewer spikes and shorter rise phases [13,19]. In the DCMD, it was found that compared to white 551 \nbackground, more complex visual background, such as the flow field, caused reduced peak firing 552 \nrate, delayed peaks, and shorter rise phases as well [28,44]. We found that flow-field-induced 553 \npeak time delay and rise phase shortening were common among multiple motion-sensitive 554 \nneurons, although the peak firing rate was not statistically affected.  555 \nThe compass cells [53] found in the central complex (CX) of locusts can use polarized light to 556 \ndetermine the orientation of the animal [54] and also respond to approaching and translating 557 \nobjects [55,56]. On average, the peak time of the compass cells was delayed in the presence of a 558 \nflow field. However, the response of most compass cells to looming objects was inhibited by the 559 \npresence of large-field motion, while some were enhanced [57]. This suggests that a group of 560 \nneurons are only responsive to looming when optic flow is present, i.e., when the animal is 561 \nmoving. Here we discriminated units (n=25) that responded to LF only, but not to LW or LG 562 \n(Fig 4). It is possible that these units can enhance motion detection during flight, and play an 563 \nimportant role in proper flight manoeuvre within a large swarm.  564 \n 565 \nUnits that responded to flow field only 566 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n27 \n \nWe found that a group of units (n=25) responded to flow field only, but not to any of the 567 \nlooming stimuli. Despite not directly responding to looming objects, these units can potentially 568 \nuse flow field to access the self-motion of the animal, and thus factor into the execution of 569 \nsuccessful collision avoidance behaviours.  570 \nNeurons that are exclusively sensitive to large-field motion have been identified in multiple 571 \nspecies, such as flies [58], moths [59], praying mantis [60], and crabs [61,62]. In hawkmoths, 572 \ntwo types of large-field motion-sensitive neurons, the horizontal cells and the vertical cells, were 573 \nidentified in the lobula plate [63]. In honeybees, horizontal regressive- and progressive motion-574 \nsensitive neurons were found to be selectively sensitive to regression or progression motion, 575 \nrespectively [64].  576 \nIn locusts, although the DCMD does not respond to the initiation of large-field grating stimuli 577 \n[65], large-field motion-sensitive neurons have been identified in the optic lobe [66–68] and 578 \nventral nerve cord [69]. The two lobula directionally selective motion-detecting neurons 579 \n(LDSMD) respond preferably to flow field moving forwards or backwards, respectively, and one 580 \nof them synapses with the protocerebral descending directionally selective motion-detecting 581 \nneuron (PDDSMD) [68,69]. Those neurons found in the medulla, however, did not display 582 \ndirectional selectivity [67]. It is worth noting that although the axon of the PDDSMD extends in 583 \nthe ventral nerve cord towards the metathoracic ganglion, it is ipsilateral to the cell body, while 584 \nwe recorded from the contralateral nerve cord. In my study, only one type of flow field was 585 \npresented, and thus we could not test for directional sensitivity. Based on previous findings, we 586 \nassume that some of these units, as well as neural ensembles, will display directional preference, 587 \nwhile others do not. This can be tested in future studies using various flow field backgrounds and 588 \nlarger-scale multichannel recordings.  589 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n28 \n \n 590 \nPopulation coding of motion-sensitive neurons 591 \nThe response of individual neurons is often “noisy”, displaying large variability over repeated 592 \nstimuli. Compared to individual neurons, functional groups of neurons, i.e., neural ensembles, 593 \ncan represent complicated information and convey robust guides to generate appropriate 594 \nbehavioural responses. In the rat gustatory cortex, neural ensembles progress through reliable 595 \nand stimulus-specific sequences of states, although the timing of transitions varies between trials 596 \n[70]. In the locust antennal lobe, multiple projection neurons encode different odorants, while the 597 \nensemble activity display complex patterns over repeated presentation [71]. We found that 598 \ndespite variability among individual neuron responses, the extracted common trends, which 599 \nrepresent ensemble activity, remain largely stable. The dominant types of common trends match 600 \nthe response of previously identified motion-sensitive interneurons, providing a reliable 601 \ninterpretation of the given stimuli.  602 \nWhile repeated co-activation of the same neurons can strengthen their synaptic connections and 603 \nincrease the likelihood of the same neural ensemble reoccurring, mechanisms like changes in 604 \nshort-term synaptic dynamics enable flexible reconfiguration of ensemble composition to meet 605 \nvarying computational needs [72]. In the rat anterior cingulate cortex, ensemble activity during a 606 \nreward-searching task is affected by previous behavioural history [73]. Physiological states can 607 \nalso determine the level of synchronization among neuron populations [74]. In Drosophila, 608 \nprevious experience and hunger state affect the ensemble coding during the determination of the 609 \nfood value [75]. Here we showed that when presented with identical looming stimuli against 610 \ndifferent backgrounds, individual unit responses and common trends were both affected. Since 611 \nthe units were generally from the same pool, changes in common trends, both the total number 612 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n29 \n \nand the peristimulus time histogram, reflect the fluidity of the contribution from individual units. 613 \nFuture studies could also examine the effect of physiological states, such as hunger or 614 \nsleeplessness, which may affect the animal’s ability to detect visual cues, likely via dynamic 615 \nensemble coding.   616 \n 617 \n 618 \n 619 \n 620 \n 621 \n 622 \n 623 \n 624 \n 625 \n 626 \n 627 \n 628 \n 629 \n 630 \n 631 \n 632 \n 633 \n 634 \n  635 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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Nat Commun. 2021;12: 4131. doi:10.1038/s41467-830 \n021-24423-y 831 \n 832 \n 833 \n 834 \n 835 \n 836 \n 837 \n 838 \n 839 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n36 \n \nSupporting information 840 \nS1 Table. Summary of spike detection and unit discrimination. On average, 27215 spikes 841 \n(waveforms) were detected, and 16.8 units were discriminated. The MANOVA results show that 842 \nthe discriminated units were statistically distinct from each other.  843 \nS2 Table. Summary of the number of units that responded to each type of visual stimulus. 844 \nLW represents loom against the white background, LG represents loom against the grey 845 \nbackground, LF represents loom against the flow field background. 846 \nS3 Table. Distribution of units in different categories when presented with looming against 847 \ndifferent visual backgrounds (LW, white; LG, half-white-half-grey; LF, flow field). The 848 \ndistribution was similar betweeno LW and LG. However, in response to LF, the proportion of 849 \nCategory 3 decreased, while the proportion of Category 5 increased. 850 \nS4 Table. Summary of dynamic factor analysis (DFA) models. For each stimulus type, the 851 \nDFA model was performed iteratively, starting with 1 common trend (CT). The Akaike 852 \ninformation criterion (AIC) and AIC corrected for small sample size (AICc) of each model are 853 \nshown above. Since AICc can prevent over-fitting, it was used to determine the best-fit 854 \napproximating model. For looming against both white and white/grey backgrounds, the best 855 \nmodels contained 7 common trends. For looming against the flow field background, the best 856 \nmodel contained 5 common trends. For flow field only, the best model contained 6 common 857 \ntrends. 858 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n37 \n \n 859 \nFigure 1 860 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n38 \n \n 861 \nFigure 2 862 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n39 \n \n 863 \nFigure 3 864 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n40 \n \n 865 \nFigure 4 866 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n41 \n \n 867 \nFigure 5 868 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n42 \n \n 869 \nFigure 6 870 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n43 \n \n 871 \nFigure 7 872 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint \n\n \n44 \n \n 873 \nFigure 8 874 \n.CC-BY 4.0 International licenseavailable under a \n(which was 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 preprintthis version posted October 17, 2024. ; https://doi.org/10.1101/2024.10.15.617990doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}