Complementary regulation of memory flexibility and stabilization by dentate gyrus granule cells and mossy cells

Complementary regulation of memory flexibility and stabilization by dentate gyrus granule cells and mossy cells | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Complementary regulation of memory flexibility and stabilization by dentate gyrus granule cells and mossy cells View ORCID Profile Douglas GoodSmith , William H. Carson , View ORCID Profile Mark E. J. Sheffield doi: https://doi.org/10.1101/2025.09.05.674488 Douglas GoodSmith 1 Department of Neurobiology and Neuroscience Institute, University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Douglas GoodSmith William H. Carson 1 Department of Neurobiology and Neuroscience Institute, University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mark E. J. Sheffield 1 Department of Neurobiology and Neuroscience Institute, University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mark E. J. Sheffield For correspondence: sheffield{at}uchicago.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF SUMMARY Accurate memory formation requires hippocampal spatial representations to balance stability, for later recall, with flexibility, to incorporate new information. The dentate gyrus (DG) is essential to memory formation, but the distinct roles of its excitatory cell types, granule cells (GCs) and mossy cells (MCs), remain unclear. To evaluate how GC and MC activity affect hippocampal output, we recorded from CA1 using two-photon calcium imaging as head-fixed mice navigated familiar and novel virtual environments. DREADD-mediated MC inhibition disrupted initial map stabilization, decreasing spatial stability in novel, but not familiar, environments. In contrast, GC inhibition increased map stability in familiar, but not novel, environments by disrupting drift of spatial maps across distinct experiences (episodes) within an environment. These results reveal how distinct DG cell types support hippocampal memory formation in context-dependent ways; MCs promote stabilization of new spatial maps to support accurate memory recall, while GCs promote flexibility to update existing representations. Introduction Place cells in the hippocampus are thought to provide a spatial framework for an internal cognitive map that underlies episodic memory and spatial navigation. 1 Spatially stable and reproducible place cell activity is required for spatial navigation and learning, 2 – 4 and increased task engagement and attention enhance place cell stability. 2 , 5 , 6 Spatial representations within the hippocampus, however, are not immutable. Changes to an animal’s external 7 – 9 or internal 5 , 10 – 12 experience alter or reorganize place cell population activity (remapping), 7 – 9 and representational drift has been observed across exposures to identical spatial contexts. 13 – 16 The balance between stability and flexibility of hippocampal representations is thought to support encoding of new experiences without disrupting previously stored memories. As the first step in the classic trisynaptic loop model of the hippocampal circuit, the dentate gyrus (DG) is thought to play an essential role in memory encoding. Notably, the DG contains two distinct populations of excitatory cells: granule cells (GCs) in the granule cell layer and mossy cells (MCs) in the hilus. 17 GCs, the most numerous cell type within the DG, promote pattern separation 18 , 19 to ensure that distinct contexts are represented in nearly orthogonal GC activity patterns. GC lesions cause significant behavioral discrimination and encoding deficits, 20 – 27 with relatively little effect on the spatial selectivity of place cells downstream in CA1/CA3. 22 , 28 MCs receive convergent inputs from GCs, 17 , 29 lateral entorhinal cortex, 30 – 32 and CA3 33 and project broadly and bilaterally throughout the DG to both directly excite 34 – 36 and disynaptically inhibit (via interneurons) thousands of GCs. 29 , 37 , 38 Many neuromodulatory inputs that signal novelty preferentially target the hilus and are capable of modulating MC excitability and activity. 29 , 39 – 41 Further, novelty and novel object exploration enhances mossy cell activity, 42 – 44 and manipulation of mossy cell activity can cause deficits in context discrimination, 37 novel object learning, 38 , 45 and anxiety-like behaviors. 45 Mossy cells are therefore well positioned to regulate DG/CA3 circuit dynamics based on novelty and task demands. Despite the proposed role of the DG in memory encoding, the specific contributions of GCs and MCs to this process remain unclear. While activity throughout the DG supports pattern separation, 32 , 46 , 47 behavioral performance is more directly tied to neuronal discrimination in CA1, 48 the primary output of the hippocampus. Examining how GC and MC activity influences CA1 place cell dynamics is therefore essential for determining the DG circuit mechanisms that support the formation of behaviorally relevant memory representations. To test this, we used two-photon calcium imaging to examine how selective, reversible inhibition of GCs or MCs affected CA1 place cell activity in head-fixed mice navigating linear virtual reality (VR) environments. We recorded place cell activity in both familiar and novel environments to assess how DG activity supports experience-specific memory demands. In novel environments, spatial maps rapidly develop and stabilize with experience, whereas in familiar environments, established maps are gradually updated to reflect ongoing learning. By comparing neural activity patterns across repeated exposures to the same environments, we assessed how the DG circuit supports the stabilization of new spatial representations and updating of existing ones. Selective, DREADD-mediated inhibition of MCs significantly disrupted the stabilization of spatial representations in novel environments, with little effect in familiar ones. In contrast, inhibition of GCs disrupted the updating of established representations, increasing stability across exposures to a familiar, but not novel, environment. Together, these results highlight distinct, cell-type specific roles within the DG circuit, with MCs supporting the stabilization of CA1 spatial representations in novel contexts and GCs promoting discrimination between similar episodes to integrate new information into existing memory representations. Results Effects of GC and MC inhibition on CA1 activity To selectively and transiently inhibit activity in MCs or GCs, a cre-dependent inhibitory DREADD virus was injected unilaterally into the dorsal DG of one of two mouse strains ( Fig. 1A-B ). Drd2-cre mice 49 , 50 were used to selectively target MCs, and Dock10-cre mice 51 were used to target GCs ( Fig. 1B , S1). While some sparse expression of Drd2 has been reported in CA1 and CA3 interneurons 50 , Drd2 expression was very specific to MCs within the DG (Fig. S1), consistent with previous studies. 36 , 42 Notably, Dock10 is primarily expressed in mature GCs, with little expression observed in immature adult-born granule cells. 52 GCaMP8f was injected into dorsal CA1 in both groups of mice ( Fig. 1A-B ) and a cannula and imaging window 53 , 54 was implanted above CA1 (see Methods). Mice were trained to navigate through a 2 m linear virtual reality (VR) track ( Fig. 1C ) for water rewards delivered at the end of each traversal (lap). When mice were reliably running 2-3 laps per minute, they were habituated to IP injections and imaging sessions began (see Methods). Download figure Open in new tab Figure 1: GC and MC inhibition distinctly alter CA1 activity and reliability, independent of remapping. (A) Viral injection strategy to inhibit GCs or MCs and image population activity in CA1. (B) Drd2-cre mice (left) and Dock10-cre mice (right) were used to inhibit MCs and GCs, respectively (red), while GCaMP8f was used to image CA1 pyramidal cell activity (green). Expression of m-Cherry (DREADD virus) is also visible in MC axons in the inner molecular layer (left) and mossy fiber projections to CA3 (right). (C) Schematic of 2-photon imaging from head fixed mice exploring virtual reality (VR) linear tracks. (D) On separate days, mice were injected with either saline (top) or DCZ (bottom). On each day, mice were alternated between a familiar (F1, F2) and a novel (N1, N2) environment. (E) Example FOV of the CA1 pyramidal cell layer. (F) Activity in CA1 (AUC, see Methods) was significantly increased following MC inhibition (purple) and decreased following GC inhibition (green). Violin plots (left) and bootstrap effect size estimation plots (right) are shown (see Methods). The effects of MC and GC inhibition were significantly different from each other (gray; permutation test p < 0.0002). (G) CA1 lap-to-lap reliability was significantly reduced following MC inhibition (purple) and increased following GC inhibition (green), with a significant difference between the effects of GC and MC inhibition (gray; permutation test p < 0.0002). (H) Example CA1 place cells. Blue indicates locations with no activity, and red indicates the cell’s peak activity. (I) Population allocation and overlap of place cell activity. The proportion of cells with a detected place field in F1 (blue), N1 (red), or both (gray) is plotted for saline (top) and DCZ (bottom) injection days, for both MC inhibition (purple, top) and GC inhibition (green, bottom) experiments. (J) There was no significant change in the remapping index between the familiar and novel environment (see Methods) following either MC inhibition (purple) or GC inhibition (green) and no significant difference between the effects of GC and MC inhibition (gray; permutation test p = 0.68). On two separate days, mice were injected 15 minutes before the start of imaging with either 0.1 mg/kg of the DREADD agonist Deschloroclozapine 55 (DCZ; to inhibit GCs or MCs) or the same volume of saline (as a within-mouse control; Fig. 1D ). Mice were alternated between a familiar and novel VR context in ∼5.5 minute blocks. Mice were first exposed to the familiar environment (the same environment used in training, F1), then a novel environment (N1, new novel environment on each day), followed by re-exposures to the familiar (F2) and novel (N2) environments ( Fig. 1D ). The first lap of the N1 session on each day was the first time the mouse had experienced that environment. During these sessions, CA1 population activity was recorded ( Fig. 1E ). For MC inhibition mice (n=6), 3382 CA1 pyramidal cells were recorded on the saline-injected control day and 3276 pyramidal cells were recorded on the DCZ injected day. For GC inhibition mice (n= 5), 5249 pyramidal cells were recorded on the saline-injected control day and 6088 pyramidal cells were recorded on the DCZ-injected day. Overall CA1 activity (AUC: area under curve; see Methods) was significantly increased following inhibition of MCs (average of F1 and N1: Saline 0.27 ± 0.01, DCZ 0.30 ± 0.01; ranksum z = 4.61, p = 3.94x10 -6 ) and decreased following inhibition of GCs (average of F1 and N1: Saline 0.34 ± 0.01, DCZ 0.30 ± 0.01; ranksum z = 8.28, p = 1.25x10 -16 ; Fig. 1F ), with a significant difference between the effects of MC and GC inhibition (permutation test p < 0.0002; Fig. 1F ). The results were not altered when analysis was restricted to either familiar or novel environments (Fig. S2 A-D). These results are consistent with previously reported effects of optogenetic GC 56 and MC 57 excitation. We next calculated the lap-to-lap spatial reliability (the mean of pairwise lap-by-lap correlation values; see Methods), for all cells with activity on at least 10% of laps ( Fig. 1G ). Reliability was significantly reduced following MC inhibition (average of F1 and N1: Saline 0.54 ± 0.004, DCZ 0.52 ± 0.004; ranksum z = 4.50, p = 6.94x10 -6 ) and significantly increased following GC inhibition (average of F1 and N1: Saline 0.50 ± 0.003, DCZ 0.52 ± 0.003; ranksum z = 4.49, p = 7.29x10 -6 ). The effects of GC and MC inhibition were significantly different from each other (permutation test p < 0.0002; Fig. 1G ). While the effects of GC/MC inhibition on reliability were observed in both novel and familiar environments (Fig. S2 E-H), the effect of MC inhibition was significantly larger for novel than familiar environments (permutation test p < 0.0002; Fig. S2E) and the effect of GC inhibition was significantly larger for familiar than novel environments (permutation test p < 0.0002; Fig. S2F). From the full population of cells, we next identified place cells ( Fig. 1H ; see Methods) and examined the allocation of place cell activity to the active population representing each environment. We compared the number of cells that had a place field only in F1, only in N1, or in both F1 and N1 ( Fig. 1I ). There were significant differences in population allocation following both MC inhibition (χ 2 (2) = 16.41, p = 2.74 x 10 -4 ) and GC inhibition (χ 2 (2) = 8.58, p = 0.014). MC inhibition primarily caused a decrease in the proportion of cells with fields selectively in the novel environment (χ 2 (1) = 10.42, p = 1.20 x 10 -3 ), accompanied by an increase in proportion of cells with fields only in the familiar environment (χ 2 (1) = 15.68, p = 7.51 x 10 -5 ). In contrast, GC inhibition primarily reduced “overlap” in the active place cell population, defined as the proportion of place cells with a field in both environments (χ 2 (1) = 8.58, p = 3.40 x 10 -3 ). While there were differences in the allocation of the place cell population following both GC and MC inhibition, robust remapping was also observed between environments (Fig. S3). There was no significant difference in remapping index (see Methods) following either MC (saline 0.52 ± 0.02, DCZ 0.56 ± 0.02; ranksum z = 1.45, p = 0.15) or GC (saline 0.43 ± 0.01, DCZ 0.45 ± 0.01; ranksum z = 1.39, p = 0.16) inhibition ( Fig. 1J ). As remapping was largely unaffected by DG disruption, the contrasting effects of GC and MC inhibition on CA1 activity ( Fig. 1F , S2A-D) and reliability ( Fig. 1G , S2E-H) likely reflect changes in trial-to-trial place cell dynamics within a context and emphasize the distinct contributions of GC and MC activity to CA1 spatial representations. MC inhibition selectively impairs place cell stability in novel environments As reproducible place cell activity across time supports memory-guided behaviors, 2 , 5 , 58 we next examined how inhibition of MCs affected the stability of CA1 place cells across repeated exposures to the same spatial context (different sessions within the same day; F1 vs. F2, N1 vs. N2). Each individual exposure to the context may be viewed as a distinct “episode” within the context. While some drift in spatial representations may provide flexibility to update existing memories 59 , 60 , excessive place cell instability across episodes may also reflect impaired memory within that context. To visualize the spatial stability of the place cell population across sessions in the familiar environment ( Fig. 2A ), we plotted all cells with a field in the first familiar session of the day, sorted by the location of the cell’s peak firing ( Fig. 2A ; F1). The activity of the same cells in the second familiar session was then plotted in the same order (F2 for familiar). To quantify place cell stability across sessions, we calculated the correlation between the average rate map in F1 and F2 for all cells with a detected field in F1 ( Fig. 2B ). MC inhibition did not significantly affect the distribution of F1/F2 correlation values ( Fig. 2B ; saline 0.64 ± 0.01, DCZ 0.65 ± 0.01; ranksum z = 0.52 p = 0.60). We next performed a bootstrap shuffling analysis (see Methods) to calculate the average correlation difference between saline and DCZ injection days ( Fig. 2B ). The average bootstrapped F1/F2 correlation difference ( Fig. 2B , bottom, red line) was not significantly different from the shuffled distribution ( Fig. 2B , bottom, histogram; p = 0.50). These results suggest that the session-to-session stability of the CA1 place cell population in the familiar environment is not significantly affected by inhibition of MCs. Download figure Open in new tab Figure 2: MC inhibition reduces place cell stability between repeated exposures to the novel environment. (A) CA1 place cell population activity, sorted by peak firing location in the first exposure to the familiar environment. Spatial activity locations are largely maintained in re-exposure to the same environment following both saline (left) and DCZ (right) injections. (B) rate map correlations (top) and bootstrap shuffling analysis (bottom) reveal no significant effect of MC inhibition in the familiar environment. (C-D) Same as (A-B), but for the novel environment. MC inhibition significantly decreased place cell stability in the novel environment (E-F) place cell correlation values (E) and effect size estimations (F) demonstrate that the effect of DCZ is significantly greater in the novel (red) than familiar (blue) environment (difference: gray). As in the familiar environment, place cells detected in the first novel session of the day (N1) evenly tiled the track on both saline and DCZ injection days ( Fig. 2C ). However, the spatial activity pattern in N2 was disrupted following MC inhibition ( Fig. 2C , DCZ N2). Indeed, the average N1/N2 rate map correlation was significantly lower following MC inhibition than on saline-injected control days ( Fig. 2D ; saline 0.56 ± 0.01, DCZ 0.46 ± 0.01; ranksum z = 6.32, p = 2.59 x 10 -10 ). Likewise, the average bootstrapped correlation difference value was significantly different from the shuffled distribution ( Fig. 2D , bottom, p < 0.001). The difference in effect sizes between novel and familiar environments was significant ( Fig. 2E-F ; permutation test p < 0.0002), indicating a selective effect of MC inhibition in the novel environment. The reduced place cell correlation in the novel environment coincided with both a slight increase in drift of place field locations (Fig. S4A) and a decrease in the place cell population overlap between N1 and N2 (due in part to new fields in N2; Fig. S4B). Importantly, while we observed disruption of place cell stability in the novel environment, MC inhibition did not lead to a complete reorganization of place cell activity. In both the novel and familiar environment, place cell correlations were significantly greater within the same environment than across environments (Fig. S3C). Together, these results reveal that inhibition of MCs selectively reduces the stability of CA1 place cell activity across repeated exposures to the same novel environment. This suggests that MC activity promotes the encoding of a stable spatial representation of the novel environment, while previously stored representations of familiar environments do not rely on MC activity. GC inhibition selectively increases place cell stability in familiar environments It is unclear if the effects of MC inhibition on place cell stability in CA1 are specific to MC inhibition or reflect the effects of any disruption of DG circuit activity. While MCs can broadly modulate GC activity, only GCs send excitatory projections outside of the DG. We therefore next examined the effects of GC inhibition on CA1 place cell stability. As observed following MC inhibition, the place cell population in the familiar environment evenly tiled the track ( Fig. 3A ) and spatial activity was largely preserved in the second familiar session ( Fig. 3A , F2). However, in contrast to MC inhibition, there was a significant increase in F1/F2 correlation values following inhibition of GCs ( Fig. 3B ; saline 0.56 ± 0.01, DCZ 0.65 ± 0.01; ranksum z = 7.40, p = 1.34 x 10 -13 ) and the bootstrapped correlation difference was significantly different from the shuffled distribution ( Fig. 3B , bottom; p < 0.001). The increase in rate map correlations was accompanied by a small decrease in the number of cells with fields in F1 but not F2 (Fig. S4C-D). Download figure Open in new tab Figure 3: GC inhibition increases place cell stability between repeated exposures to the familiar environment. Same format as Fig. 2 , but for GC inhibition mice. (A) sorted place cell population activity in the familiar environment (B) rate map correlations (top) and bootstrap shuffling analysis (bottom) reveal significantly increased place cell stability across sessions in the familiar environment (C-D) Same as (A-B), but for novel environment. There was no significant effect of GC inhibition in the novel environment (E-F) The effect of GC inhibition is significantly greater in the familiar (blue) than novel (red) environment (difference: gray). In the novel environment ( Fig. 3C-D ), however, there was no significant difference between N1/N2 correlation values for cells recorded on saline vs. DCZ injected days ( Fig. 3D ; saline 0.54 ± 0.01, DCZ 0.53 ± 0.01; ranksum z = 0.47, p = 0.64) and the bootstrapped correlation difference value was not significantly different from the shuffled values ( Fig. 3D , bottom; p = 0.39). The difference in effect sizes in novel and familiar environments was significant ( Fig. 3E-F , permutation test p < 0.0002), indicating a selective effect of GC inhibition on place cell correlations in the familiar environment. There were significant differences between the effect size observed following GC vs. MC inhibition in both the familiar (Fig. S5A; permutation test p < 0.0002) and novel (Fig. S5B; permutation test p < 0.0002) environments. These results were consistent when analysis was expanded to include any cell with a field in either session within an environment (rather than restricted to cells with a field in the first of the two sessions; Fig. S5C-D) and across individual mice (Fig. S6). Together, these results suggest that GC inhibition selectively increased the stability of the CA1 place cell population across distinct episodes in the familiar environment, in contrast to the reduced stability observed in the novel environment following MC inhibition. CA1 population activity is differentially affected by GC and MC inhibition While both MC and GC inhibition affected place cell stability across sessions, place-cell analyses may miss more distributed shifts in CA1 population activity and may not fully reflect the functional output of the hippocampal circuit. To address this, we analyzed CA1 stability at the population level by quantifying population vector (PV) correlations using all recorded cells (pooled across animals but see Fig. S6). This analysis captures broader ensemble-level changes in CA1 spatial representations, including potential contributions from non-place cells, 61 and avoids biases introduced by the specific method used for place cell identificiation. 62 The population vector for each spatial bin in the first session (x-axis; F1 for familiar, N1 for novel) was correlated to each spatial bin for the second session (y-axis; F2 for familiar, N2 for novel). The distribution of correlation values at equivalent locations in the two sessions (values along the main diagonal of each PV correlation plot) reflects the stability of the population spatial representation of the track. In MC inhibition mice ( Fig. 4A-D ), a strong correlation band is observed in the familiar environment following both saline and DCZ injections ( Fig. 4A , left). Download figure Open in new tab Figure 4: MC and GC inhibition differentially affect CA1 PV stability in novel and familiar environments. (A-D) PV correlation analysis for MC inhibition mice. (A) PV correlation matrices for repeated exposures to the familiar (left) or novel (right) environments following saline (top) or DCZ (bottom) injections. PVs for each position along the track in the first session (F1 or N1; x-axis) were correlated with PVs for each position in the second session (F2 or N2; y-axis). The band of high correlation at corresponding locations between sessions (main diagonal) was disrupted in the novel environment following MC inhibition (bottom right). (B) Distribution of correlation values along the main diagonal (at corresponding locations). Arrows in B reflect the direction of the effect of DCZ relative to saline. PV correlations were reduced in the novel environment and increased in the familiar environment. (C) Effect size estimation plots for effects of MC inhibition. The effects was significantly greater in the novel environment than the familiar environment (gray). (D) Bootstrap shuffling analysis (see Methods) confirmed that MC inhibition significantly affected PV correlations in the familiar (left) and novel (right) environments. (E-H) same as (A-D) but for GC inhibition mice. PV correlations were increased following GC inhibition in both novel and familiar environments (E, F), but the effect was significantly larger in the familiar environment (G; gray). Bootstrap shuffling analysis showed a significant increase in correlation in the familiar environment (left) but only a non-significant trend in the novel environment (right). (I-J) Direct comparison of the effects of MC and GC inhibition in the familiar (I) and novel (J) environments. GC inhibition had a significantly larger effect than MC inhibition in the familiar environment (I) and MC inhibition had a significantly larger effect than GC inhibition in the novel environment (J). In contrast, the correlation band was significantly disrupted in the novel environment following MC inhibition ( Fig. 4A ; bottom right, DCZ). PV correlation values along the main diagonal were significantly reduced in the novel environment following MC inhibition relative to saline-injected control sessions ( Fig. 4B ; saline 0.53 ± 0.01, DCZ 0.41 ± 0.01; ranksum z = 7.96, p = 1.73 -15 ), consistent with place cell rate map correlations ( Fig. 2 ). Interestingly, PV correlation values were also slightly but significantly increased in the familiar environment ( Fig. 4B ; saline 0.64 ± 0.01, DCZ 0.67 ± 0.01; ranksum z = 3.17, p = 1.5x10 -3 ). Bootstrap shuffling analysis confirmed a significant effect of MC inhibition in both the familiar (p = 0.014) and novel (p < 0.001; Fig. 4D ) environments. However, the effect of MC inhibition in the novel environment was significantly different than the effect in the familiar environment ( Fig. 4C ; permutation test p < 0.0002). Following GC inhibition ( Fig. 4E-H ), strong PV correlation bands were observed in both the familiar and novel environments ( Fig. 4E ). As reported for place cell rate map correlations ( Fig. 3 ), PV correlation values were significantly increased in the familiar environment following GC inhibition ( Fig. 4F ; saline 0.51 ± 0.01, DCZ 0.64 ± 0.01; ranksum z = 7.62, p = 2.57 -14 ). GC inhibition also caused a small significant increase in the PV correlation values for novel environments ( Fig. 4F ; saline 0.46 ± 0.01, DCZ 0.49 ± 0.01; ranksum z = 3.56, p = 3.77 -4 ), but the effect of GC inhibition was significantly greater in the familiar environment than the novel environment ( Fig. 4G , permutation test p < 0.0002). While a significant effect of GC inhibition on PV correlations was also observed for bootstrap shuffled data in the familiar environment ( Fig. 4H , left; p < 0.001), the effect in the novel environment did not reach significance ( Fig. 4H , right; p = 0.057). The effects of both MC and GC inhibition on PV correlations were consistent across individual mice (Fig. S6) and when analysis was restricted to place cells (Fig. S7). In contrast to rate map correlations, small but significant effects on PV correlation were also observed in the familiar environment following MC inhibition and in the novel environment following GC inhibition. While both MC inhibition and GC inhibition increased PV correlations in the familiar environment, the effect was significantly larger following GC inhibition than MC inhibition ( Fig. 4I ; permutation test p < 0.0002). Likewise, while both GC and MC inhibition significantly impacted PV correlations in the novel context (but see non-significant trend in bootstrap shuffling analysis; Fig. 4H ), the effect was in the opposite direction and significantly different following MC inhibition and GC inhibition ( Fig. 4J ; permutation test p < 0.0002). While largely consistent with the reported effects on place cell stability ( Fig. 2 - 3 ), these effects suggest that the PV correlation analysis may be more sensitive to small changes in the CA1 representation than rate map correlations. This may be driven in part by our place field selection criteria 62 or by contributions of non-place cells 61 (i.e. engram cells, interneurons, head direction cells, etc.) to the spatial representations in CA1. MC inhibition disrupts stabilization of population activity during encoding of novel environments While GC and MC inhibition have opposing effects on the stability of spatial representations, it is unclear how these differences evolve over time. To examine the temporal dynamics of population stability, we computed PV correlations between session-wide spatial maps and lap-by-lap activity. For both the saline and DCZ recording day, a reference PV (PV ref ) was created from the average rate map of all cells during the initial exposure to each environment (F1 for familiar, N1 for novel; Fig. 5A ). PV ref was then compared to a series of PVs generated from a five-lap rolling average rate map ( Fig. 5A ; see Methods) both within the same session (F1, N1) and across repeated exposures to the same environment (F2, N2). This analysis was restricted to the minimum number of laps completed by all mice on both the saline and DCZ injection day. PV correlation curves were created by taking the average of values at corresponding locations in each PV matrix ( Fig. 5A , S8). Download figure Open in new tab Figure 5: MC inhibition prevents stabilization of novel spatial maps. (A) Method for calculating lap-by-lap PV correlations. Left: Reference PVs (PV ref ) were created for the saline and DCZ days from the average rate map across all laps in the first session (F1 or N1). Right: PV ref was correlated to a series of PVs created from five-lap rolling average rate maps in either the same session (top) or repeated exposure to the same environment (bottom). Values at corresponding locations (along the main diagonal) were used for further analysis. (B) Average PV correlation between PV ref and PV generated from each five-lap average rate map. The PV correlation curves are plotted for both familiar (left) and novel (right) environments, following both saline (dark colors) and DCZ (light colors) injections. Error bars represent confidence intervals. (C) Top: distribution of shuffled MSE values (histogram) is compared to the observed MSE between the saline and DCZ curves (red line). MC inhibition affected PV correlation curves in both the familiar (left) and novel (right) environments. Bottom: distribution of differences between the observed effect size (relative to shuffles) in the novel and familiar environments. Positive values indicate stronger effects in the novel environment. MC inhibition caused a stronger effect in the novel environment across all shuffles (all values above 0, red line). (D) Effect size estimation plots for correlations between PV ref and a PV created from the first half or last half of all laps in the session. The effect of MC inhibition relative to saline and effect size differences between the first and last half correlations are plotted for familiar (left) and novel (right) environments. While MC inhibition caused similar effects in the first and last half of the familiar environment, the effect because significantly stronger across time in the novel environment. (E-G) Same as B-E but for correlation between PV ref from the first session (F1, N1) and PVs from repeated exposure to the same environment (F2, N2). MC significantly affected lap-wise PV curves in the novel, but not familiar, environment (E-F) with a larger effect observed in the novel environment across all shuffles. The effect of MC inhibition decreased over time in the familiar environment but persisted from the first to last half of the novel session. To evaluate how spatial representations develop and stabilize within a session, we first compared the session-wide PV ref to lap-by-lap PVs from the same session ( Fig. 5B-D ). We quantified the effect of MC inhibition by calculating the mean squared error (MSE) between PV correlation curves from saline and DCZ days. The observed MSE was compared to a shuffled distribution created by randomly reassigning cells between conditions (see Methods). In both familiar and novel environments, the observed MSE was greater than all values from the shuffled distribution (p < 0.001; Fig. 5C ), indicating a significant effect of MC inhibition on lap-by-lap population dynamics. To compare the relative strength of this effect across environments, we calculated the difference between the observed and shuffled MSE values for each environment and subtracted the familiar from the novel value (see Methods; positive values indicate stronger effect in the novel environment). The effect of MC inhibition was larger in the novel environment for all shuffles ( Fig. 5C ), consistent with the selective effects of MC inhibition in the novel environment reported above. While PV correlations increased throughout the session ( Fig 5B , S8), MC inhibition reduced the maximum correlation to the session-wide PV ref that could be achieved, reflecting disrupted stabilization of the novel spatial map. To further evaluate the temporal dynamics of population activity throughout the session, we next calculated the correlation between the session-wide PV ref and PVs constructed from either the first or last half of all laps( Fig. 5D ; see Methods). While rolling-average PV correlations were restricted to laps completed by all animals, this analysis used all laps from each mouse to evaluate population dynamics across the entire episode in the environment. In the familiar environment, MC inhibition slightly reduced PV ref correlations with both the first (ranksum z = 3.34, p = 8.26x10 -4 ) and last half (ranksum z = 4.84, p = 1.33x10 -6 ) of the session, but there was no significant change across time (permutation test p = 0.15, Fig. 5D ). In contrast, in the novel environment, MC inhibition did not significantly affect the PV ref correlation with the first half (ranksum z = 0.67, p = 0.50), but significantly reduced the correlation with the last half (ranksum z = 6.04, p = 1.56x10 -9 ; Fig. 5D ) of the session. This time-dependent difference in effect size (permutation test p < 0.0002) further suggests that MC inhibition disrupted the stabilization of the novel spatial map over the course of the session. While MC inhibition prevented the full stabilization of new spatial maps within a novel session, its effects on the reinstatement and updating of spatial representations across repeated exposures to the same environment is unclear. We, therefore, next compared lap-by-lap PVs from the second session (F2, N2) to the session-wide PV ref from the first session (F1, N1; Fig. 5E-G ). MC inhibition did not significantly alter PV correlation curves in the familiar environment ( Fig. 5F ; p = 0.28), but significantly reduced correlations in the novel environment ( Fig. 5F ; p < 0.001). The magnitude of the effect in the novel was greater than in the familiar across all shuffles ( Fig. 5F , bottom), further supporting the novel environment-specific effects of MC inhibition reported above. While MC inhibition significantly reduced PV correlations in both the first (ranksum p = 6.94, p = 3.85x10 -12 ) and last half (ranksum z = 6.65, p = 2.85x10 -11 ) of the novel session, the magnitude of this reduction did not differ significantly across time (permutation test p = 0.15; Fig. 5G ). In contrast, PV correlations with the first half of the familiar session were significantly increased (ranksum z = 4.10, p = 4.06x10 -5 ), consistent with the full-session effects in Fig. 4 . However, correlations with the last half of the familiar session were not significantly affected (ranksum z = 0.76, p = 0.45; difference: permutation test p < 0.0002; Fig. 5G ) suggesting a transient increase in map stability. As a reduced PV correlation is already present at the start of the second novel session and remains unchanged throughout that session, the observed effects primarily reflect the impaired stabilization of the spatial map during encoding, rather than a direct disruption of retrieval. While correlations to the session-wide PV ref capture the similarity of lap-wise activity to the overall representation of the episode, these results could also be influenced by variability during early laps when the majority of place field form. 16 , 63 We therefore also calculated lap-wise PV correlations to a PV created from the last five laps in the session (Fig. S9), which did not significantly alter the results. GC inhibition disrupts updating of representations across episodes in familiar environments Given the contrasting effects of GC and MC inhibition on session-wide population stability ( Fig. 4 ), we next examined how GC inhibition affects the temporal dynamics of CA1 population activity. GC inhibition slightly, but significantly, affected lap-wise correlations to PV ref within the same (first) session in both familiar (p = 0.02) and novel (p = 0.04) environments ( Fig. 6A–B ). The effect of GC inhibition was greater in the familiar than novel environment for all but 68/1000 shuffles, suggesting a non-significant trend toward a stronger effect in the familiar environment ( Fig. 6B , bottom). Download figure Open in new tab Figure 6: GC inhibition prevents updating of familiar representations. (A-C) Same as Fig. 5B-D but for GC inhibition. Correlation between activity in the first exposure to the familiar or novel environment (F1, N1) to lap-by-lap activity in the same session. Small but significant effects of GC inhibition were observed in both novel and familiar environments, with a non-significant trend toward stronger effects in the familiar environment (B). The effect of GC inhibition decreased over time in the familiar environment but was unchanged across time in the novel (C). (D-F) Same as Fig. 5E-G but for GC inhibition. Correlation between activity in the first session (F1, N1) to lap-by-lap activity in the repeated exposure to the same environment (F2, N2). GC inhibition significantly altered PV correlation curves in familiar, but not novel environments, with significantly stronger effects observed across all shuffles in the familiar environment (E). PV correlations were significantly elevated in both the first and last half of the session, with a significant increase in the effect across time, while no significant effects were observed in either the first or last half of laps in the novel environment (F). In the familiar environment, GC inhibition slightly but significantly increased the PV ref correlation with the first half of the session (ranksum z = 2.79, p = 0.01), with no significant effect in the last half (ranksum z = 0.78, p = 0.43, difference: permutation test p < 0.0002, Fig. 6C ). In contrast, in the novel environment GC inhibition significantly reduced PV ref correlations to both the first (ranksum z = 4.23, p = 2.35x10 -5 ) and last (ranksum z = 3.63, p = 2.81x10 -4 ) halves of the session ( Fig. 6C ). However, the magnitude of this reduction did not differ significantly across time (permutation test p = 0.51; Fig. 6C ). The slight contrast with the PV correlation curves in Fig. 6A reflects the laps included in each analysis. While correlation curves are based on the initial laps common to all animals (12-15 laps across conditions), this analysis compares the start vs. end of the session across all laps (12-44 laps across mice) for each animal. Across repeated exposures to the same environment ( Fig. 6D-F ), GC inhibition significantly affected lap-wise PV correlations in the familiar (p < 0.001) but not novel (p = 0.51) environment ( Fig. 6E ). The magnitude of the effect was greater in the familiar than novel environment across all shuffles ( Fig. 6E , bottom). The sustained and selective increase in PV correlations in the familiar environment is consistent with effects of GC inhibition reported above. As with MC inhibition, the results were comparable when lap-wise PV correlations were calculated relative to the last five laps rather than the session-wide PV ref (Fig. S9). In the novel environment, PV ref correlations with both the first and last half of the session were unaffected by GC inhibition (first half: ranksum z = 0.27, p = 0.78; last half: ranksum z = 1.30, p = 0.19; Fig. 6F ). In contrast, in the familiar environment, PV correlations were significantly elevated for both the first (ranksum z = 5.33, p = 1.00x10 -7 ) and last (ranksum z = 6.53, p = 6.63x10 -11 ) halves of the session, with a significantly increasing effect over time (permutation test p < 0.0002; Fig. 6F ). These findings suggest that GC inhibition does not impair the formation or within-session stabilization of novel spatial maps but instead interferes with the updating of established maps across distinct episodes. As a result, spatial representations in the second familiar session remain more similar to those observed during the first session of the day. Discussion In this study, we investigated how transient, reversible inhibition of GCs and MCs affected CA1 spatial representations of novel and familiar environments. MC inhibition significantly disrupted CA1 lap-to-lap reliability (within a session) and stability (across repeated exposures to the same environment), particularly in novel contexts. Temporal population dynamics suggest that this impairment was caused by disruption of the initial stabilization of spatial maps during the first exposure. In contrast, GC inhibition increased spatial stability and reliability, predominantly in the familiar environment. Lap-wise population dynamics indicate that GC inhibition impaired the updating of established spatial maps across repeated exposures to a familiar environment, leading to an elevated and sustained similarity to the representation of the first session of the day. Together, these results suggest specific roles for GCs and MCs in episodic memory formation, with MCs promoting stability of novel spatial representations while GCs may support discrimination between similar experiences to integrate new information into established representations. One of the primary functions of the DG is pattern separation, 18 , 64 with activity throughout the broader DG/CA3 circuit supporting this function. 32 , 46 , 47 , 65 , 66 Sparse GC activity is thought to be essential to this function, preventing interference between similar experiences. 19 , 32 , 67 – 72 GCs are the primary target for entorhinal inputs to the DG and are the only cell type to send projections downstream to CA3. 17 Reduced CA1 activity following GC inhibition ( Fig. 1F ) suggests a reduction in feedforward activity throughout the trisynaptic circuit, consistent with reduced population sparsity observed following optogenetic GC activation. 56 Likewise, the increased CA1 activity following MC inhibition is consistent with the proposed net inhibitory effect of MCs on GC activity, 37 , 69 and the effects of optogenetic MC activation. 57 While the effects of GC and MC inhibition on CA1 activity were small, the results were significant despite the relatively localized, unilateral nature of our manipulation. While previous studies have reported significant behavioral effects of chronic GC disruption, 20 – 26 , 73 – 75 the spatial selectivity of CA1 place cells was largely unaffected by these manipulations 22 , 28 (but see Chen et al, 2025 76 ). However, GC lesions often lack cell-type specificity and allow for compensatory adaptations to DG circuit disruption. While recent chemogenetic and optogenetic studies have confirmed the behavioral effects of GC and MC manipulation, 37 , 77 – 79 the effects on hippocampal spatial representations remain unclear. Interestingly we found that transient, cell-type specific DG inhibition produced more pronounced effects on CA1 place cell stability than previously observed following more widespread and long-lasting DG disruptions. 22 , 24 , 28 Importantly, remapping between environments was unaffected by GC and MC inhibition and place cell activity did not fully remap between sessions in the same context, suggesting that DG disruption did not affect the fundamental ability to remap and form an initial representation of space, but rather modulated the stabilization and flexibility of these neural representations. GC inhibition increased place cell reliability and stability, particularly in the familiar environment. Notably, while DG pattern separation is often associated with memory encoding, we saw little effect of GC inhibition in the novel environment. DG pattern separation is thought to be required for discrimination following small, but not large changes in input. 27 , 72 , 80 As the novel and familiar VR environments were highly dissimilar, CA3 may be capable of performing pattern separation to form new a spatial map in the novel environment independent of the DG via direct EC inputs to CA3. 18 , 81 In contrast, although the spatial cues in the familiar context were always identical, every time the mouse is exposed to the context is a distinct memory “episode”. Small differences in non-spatial cues and internal motivation between episodes may be reflected in GC activity, where cue, 82 object, 83 and reward 84 related activity have been reported. These changes in GC activity may conflict with stable CA3 activity patterns, disrupting place cell stability throughout the hippocampal circuit. GC inhibition may, therefore, attenuate these conflicting inputs to CA3, allowing CA3 pattern completion to promote stable place cell activity throughout the hippocampus. Consistent with this idea, lap-by-lap and session-wide population analysis revealed that GC inhibition elevated CA1 spatial map stability across repeated exposures to the same familiar environment, suggesting impaired updating of spatial representations across episodes. Thus, GCs enhance representational differences across distinct episodes, consistent with their suggested role in fine mnemonic discrimination. By promoting variability in hippocampal representations of the same environment, GC activity may drive representational drift in the hippocampus. While space and context can be reliably decoded from hippocampal population activity over long periods of time, 14 , 85 significant drift of place cell activity occurs even within the same spatial context. 13 , 14 , 16 , 86 While it is possible that drift merely represents noise in the hippocampal system, it may also reflect an active process that allows novel information to be flexibly incorporated to existing memory representations. 60 , 87 Increased place cell stability following GC inhibition may reflect impaired drift of the CA1 representation of familiar contexts and suggest drift is a valuable and dynamic memory process that relies on GC activity. These results may also point to manipulation of GC activity as a potential way to experimentally modulate drift to further evaluate the role of drift in memory-guided behaviors. One of the proposed roles for the DG is novelty detection, 64 , 88 and changes in DG activity may underlie the reported effects of novelty on place field dynamics in CA1 and CA3. 16 , 63 , 89 Many neuromodulatory inputs to the hippocampus target MCs, modulating their excitability, plasticity, and synaptic transmission. 39 , 40 , 90 These changes may shift the excitation-inhibition balance in the DG 91 , 92 and alter the influence of CA3 backprojections 33 , 66 , 93 , recalibrating DG/CA3 circuit dynamics to promote encoding in novel environments. In this study, CA1 place cell stability and reliability was reduced, particularly in novel environments, following MC inhibition. Lap-wise population analysis revealed that this instability emerged over the course of the first novel session, suggesting that MC activity is critical for the initial stabilization of spatial representations during encoding. While PV correlations were reduced in the second novel session as well, these effects were apparent even from the first laps and remained constant throughout the session. This suggests that reduced stability in the second session was not driven by a specific retrieval deficit but rather reflects the idea that reliable memory recall depends on the prior formation of a stable spatial representation during encoding. Together, these results support an important role for MC activity in forming stable spatial representations of novel environments. MCs do not project outside of the DG, but can broadly regulate GC activity to control communication within the DG/CA3 circuit. 29 , 90 MCs provide both strong local inhibition (via interneurons) and broad, bilateral, weak excitation. 29 , 36 This circuit may allow MCs to promote coordinated and sparse GC activity to support pattern separation. 47 , 94 In contrast, GC mossy fibers generally project within a hippocampal lamella, 95 likely encompassing the region of CA1 we are recording. The contrasting results we observed between GC and MC inhibition may, therefore, reflect differences in the scope of DG regulation: MC inhibition disrupted the coordination of activity within the broader DG/CA3 circuit, while GC inhibition caused localized effects on GC population output. Following GC inhibition, local feedforward input to CA3 is reduced, but circuit-level computations may be largely preserved by MC-driven coordination. Reduced GC activity has minimal effects in the novel environment, where CA3 can form and stabilize new spatial maps independently of the DG, but in the familiar environment it reduces the mismatch with pattern completed CA3 activity patterns, enhancing place cell stability. In contrast, MC inhibition may broadly perturb GC/CA3 communication, disrupting both broad GC coordination and feedback from CA3. This disruption may be particularly detrimental in novel environments due to the central role for MCs in novelty detection. 42 , 43 Future studies including direct recordings from DG and CA3 cells will be necessary to further disentangle these complex DG/CA3 circuit interactions during encoding and retrieval. For a memory system to be useful in guiding future behavior, it must maintain a balance between preserving past memories and adapting to new information. While this balance is often framed as a dichotomy between memory encoding and retrieval, both encoding and retrieval are ongoing processes that require a dynamic and task-dependent equilibrium between stability and flexibility. Our results support an essential role for the DG circuit in memory encoding, with cell-type specific roles in novel and familiar contexts. MC activity supports the formation and stabilization of place cell representations in novel environments by broadly regulating GC activity. In contrast, the more localized effects of GC inhibition lead to increased stability of CA1 representations across repeated exposures to familiar contexts, suggesting a role for GCs in encoding small differences between distinct episodes in the same spatial context. These findings provide insight into how the functional connectivity of the DG enables the hippocampus to flexibly encode, maintain, and update spatial memories. Contributions Douglas GoodSmith: Conceptualization, investigation, formal analysis, visualization, writing - initial draft, writing – review & editing. Will Carson: investigation. Mark EJ Sheffield: Conceptualization, supervision, Writing – review & editing. Conflict of interest The authors declare no competing interests. Methods Subjects For this study, we used male and female Drd2-cre 50 (MMRRC 032108-UCD) and DOCK10-cre 51 (gift from Tonegawa lab) mice. At the time of surgery, mice were 12-16 weeks old, weighing 20-28g, and were maintained on a C57BL/6 background. In total, we recorded from 6 Drd2-cre mice (1 male and 5 female) and 5 Dock10-cre mice (1 male and 4 female). Mice were on a reverse 12-hour light/dark cycle and were group housed prior to surgery and individually housed after surgery. All experimental procedures were performed during the animal’s dark cycle. All surgeries and animal procedures complied with National Institutes of Health guidelines and were approved by the Institutional Animal Care and Use Committee at the University of Chicago. Surgical procedures During virus injection surgeries, mice were initially anesthetized with ∼3% isoflurane and injected with saline (0.5 ml, intraperitoneal) and Meloxicam (1-2 mg/kg, subcutaneous) before being weighed and mounted to a stereotaxic surgical station (David Kopf Instruments). A surgical plane was maintained using isoflurane (∼1-2%). A small incision was made to expose the skull, which was cleaned and leveled before a small craniotomy was drilled over the right hippocampus (centered at the injection target site: 2.0mm posterior and 1.25 mm lateral of Bregma). To target the DG for MC (in Drd2-cre mice) or GC (in Dock10-cre mice) inhibition, 150nl of a cre-dependent inhibitory DREADD virus (hSyn-DIO-hM4D(Gi)-mCherry; Addgene, #44362) was injected 2.0mm below the surface of the brain. To target CA1 for imaging, 75nl of GCaMP8f (CAMKIIa-jGCaMP8f; Addgene, #176750) was injected 1.25mm below the surface of the brain. Viruses were injected slowly by applying gentle pressure with a syringe to a beveled glass micropipette. Following each injection, the pipette was left in place for 10 minutes to allow the virus to diffuse before slowly retracting the pipette from the brain. After both injections, the skull was covered with dental cement (Metabond, Parkell Corporation) and a titanium headplate (Atlas Tool and Die Works) was attached. Following surgery, mice were given restricted access to water (0.8 - 1.0 ml of water per day) and were weighed daily to maintain their weight 75-80% of their free-water weight. Mice received supplemental water for two days post-surgery (to aid recovery) and if their weight or body condition changed considerably. After the mice had recovered from the virus implantation surgery and their weight had stabilized (5-8 days), they underwent a second surgery to implant an imaging window/cannula. 53 Mice were anesthetized and injected with meloxicam (1-2 mg/kg) as described above. Saline was not provided during the cannula implantation surgery to prevent excessive bleeding. The headplate was removed and the exposed skull was thoroughly cleaned before being dimpled with a hand drill. A trephine drill (Fine Science Tools) was used to create a 2.7-2.8mm craniotomy positioned over the site of the injection craniotomy. The dura was removed with a bent 27-gauge needle, and cortex overlying the hippocampus was aspirated slowly. The brain was regularly irrigated with saline during aspiration, which continued until the external capsule was exposed and oriented fibers were visible. The top-most layers of the external capsule were peeled away until the visible fibers were oriented at a ∼45° angle when viewed from above. The surface of the brain was cleaned using surgical foam (Surgifoam, Ethicon). A stainless-steel cannula (2.77 mm OD, 2.31 mm ID, 1.5mm length, MicroGroup, Inc.) was prepared by gluing a small glass circle (2.5mm diameter, Potomac Photonics) to one side of the cannula using a UV-curing epoxy (NOA81, Norland). This cannula was lowered into the craniotomy, so that the top surface rested just above the skull surface. A thin layer of metabond was used to secure the cannula and attach a new headplate. In addition, a small metal ring (19 mm diameter) was attached to the headplate to house the microscope objective and block out ambient light during imaging. Mice were handled daily following surgery to acclimate them to the experimenters and to handling. After ∼7 days of recovery, mice began behavior training. Behavior and training Behavior training and imaging was performed in a virtual reality (VR) setup as previously described. 16 , 63 Mice navigated through 2m long virtual linear environments (created using VIRMEn 96 ) that were displayed on 5 bezel-free computer monitors arranged around the mouse. Mice were head fixed with their limbs resting comfortably on a freely rotating Styrofoam wheel (treadmill). Movement of the wheel was measured with a rotary encoder (US Digital) and used to update the mouse’s position within the VR environment. A lick spout (Kent scientific) was positioned in front of the mouse, and water rewards were delivered using a solenoid (NResearch). The lick spout was attached to a capacitive touch sensor circuit (AT42QT1010, Sparkfun electronics) to track licking behavior. Movement, reward delivery, and licking behavior data were recorded and synchronized to imaging frames using a Picoscope digital oscilloscope (PICO4824, Pico Technology). Following each traversal (“lap”) of the 2m virtual linear track, a 4ul water reward was given and the VR paused for 1.5s to allow for water consumption and to help distinguish each lap from one another. Mice were then virtually teleported back to beginning of the track to begin another lap. Mice were trained to navigate the virtual environments for ∼30 minutes per day. For the first 2-3 days, a shortened ∼1.4 m track was used to help the mice complete laps and associate running with water rewards. The track was then lengthened to 2m and training continued until mice were running 3-5 laps per minute without extended pauses. Mice generally took 10-14 days of training to reach this level (some took longer, and others never achieved this level of performance and were not used for imaging). For the final 3-4 days of training, mice were habituated to IP injections (injected IP with 0.05 ml of saline 15 minutes before the start of training) and to the imaging objective (lowered to the mouse’s head during training). Following this habituation period, imaging sessions began. Two-photon imaging Imaging was performed using a laser scanning two-photon microscope (Neurolabware). Images were collected at a frame rate of 31 Hz with bidirectional scanning through a 16x/0.8 NA/3 mm WD water immersion objective (MRP07220, Nikon). GCaMP8f was excited at 920 nm with a femtosecond-pulsed two photon laser (Insight DS + Dual, Spectra-Physics). Emitted fluorescence was collected using GaAsP PMTs (H11706, Hamamatsu). The average power of the laser measured at the objective was between 50–80 mW. Time-series images were collected through Scanbox (v4.1, Neurolabware) and the PicoScope Oscilloscope (PICO4824, Pico Technology) was used to synchronize frame acquisition timing with behavior. During imaging, ambient light from the VR system was blocked using a ring of tape extending from the microscope objective to the head ring implanted on the mouse. On imaging days, mice alternated between a familiar and novel VR environment in ∼5.5 minute (10,000 imaging frames) blocks. A recording day consisted of a session in the familiar environment (F1), a session in the novel environment (N1), a repeated exposure to the familiar environment (F2), and a repeated exposure to the novel environment (N2). These sessions were always presented in this order, and a new novel environment was used on each imaging day. The familiar environment was always the same (the environment the mice were trained in). The novel environments had distinct visual cues, colors, and visual textures, but the same dimensions (2m linear track) as the familiar environment and had a similar number of local and distal visual cues. DREADD experiments The ligand Deschloroclozapine dihydrochlorine (DCZ, MedChemExpress) was used to activate the inhibitory DREADD receptor hM4D(Gi). DCZ was used over the more commonly used Clozapine N-Oxide (CNO) as DCZ is effective at activating DREADD receptors at much lower doses than CNO, 55 , 97 and due to the slow kinetics and known off-target effects of CNO. 98 DCZ was dissolved in DMSO at 5mg/ml and stored at -20° C. On the day of the experiment, DCZ solutions were thawed at room temperature and diluted to 0.04 mg/ml with saline. Mice were injected with 0.05 – 0.1 ml of this DCZ solution (0.1 mg/kg) 15 minutes before the start of imaging. In the same mice, on separate days, mice were injected with an equal volume (0.05 – 0.1 ml) of saline 15 minutes before the start of imaging. A new novel environment was used on each recording day, and the order of DCZ and saline-injected recordings were randomized between mice. Perfusion and histology Mice were anesthetized with isoflurane and perfused with ∼10mL of phosphate-buffered saline (PBS) followed by ∼20 ml of 4% paraformaldehyde (PFA) in PBS. Brains were extracted and stored in PFA overnight before being transferred to a 30% sucrose solution. Following 3-5 days in the sucrose solution, brains were frozen and sectioned into 30-50um thick slices on a cryostat (Leica CM 3050S). To confirm the selectivity of Drd2-cre expression to mossy cells, a separate group of Drd2-cre mice was injected with a Cre-dependent GCaMP virus in the DG (Fig. S1). To prevent overlapping colors between staining and viral expression, only a single injection in the DG was used (no injection in CA1). Slices from these mice were collected into well plates of PBS for immunohistochemistry. Slices were washed 5 times with PBS for 5 minutes, then blocked in a solution (1% bovine serum albumin, 10% normal goat serum, 0.1% Triton X-100) at room temperature for 2 hours. Slices were then incubated with 1:100 rabbit anti-Glur2/3 polyclonal antibody (AB1506; Sigma-Aldrich) or 1:200 rabbit anti-GAD67 (PA5-21397, Invitrogen) in blocking solution at 4° C for 48 hours. Slices were then washed 5x in PBS and incubated with 1:1000 goat anti-rabbit secondary antibody (A-21245, Invitrogen) for 2 hours. All slides were mounted using a mounting media with DAPI (DAPI-Fluoromount, SouthernBiotech). Brain slices were imaged with a Caliber I.D. RS-GF Large Format Laser Scanning Confocal microscope at the Integrated Light Microscopy Core at the University of Chicago. Image processing and ROI selection Time-series images were preprocessed using Suite2p. 99 Movement artifacts were removed using rigid and non-rigid transformations and recordings were assessed to ensure the absence of drifts in the z-direction. Datasets with visible z-drifts were discarded. Regions of interest (ROIs) were also identified and classified as cells or non-cells using suite2p. The data was manually inspected for accuracy of the cell classification, and erroneously identified cells were manually excluded. Baseline corrected ΔF/F were produced for each ROI, and these traces were filtered for significant calcium transients, as previously described. 16 , 54 Briefly, fluorescence values were normalized by the 8 th percentile of values over a ∼15 second sliding window and the median value was subtracted to remove slow changes in fluorescence traces. Baseline and standard deviations were then calculated from periods that did not contain large transients. Putative significant calcium transients were identified as events that started when fluorescence exceeded 2 standard deviations above baseline and ended when fluorescence returned to less than 0.5 standard deviations from baseline. Baseline corrected fluorescence traces were further subjected to an analysis of the ratio of positive to negative deflecting transients of various amplitudes to identify significant transients with <0.1% false positive error rates, as described previously. 54 , 100 As brain movement and noise can produce small fluorescence transients in both the positive and negative directions (equally) and GCaMP should only produce positive deflections, any negative deflections are assumed to be noise. By identifying transients of a duration and amplitude that occur in <1% of negative deflections, significant calcium transients can be reliably identified. Fluorescence values outside of these identified significant events were set to 0 for further analysis. Imaging data were aligned to behavior data and speed filtered to remove periods of immobility or slow running (<2 cm/s). Data analysis Rate maps Activity rate maps were created for each cell by binning all activity along the track into 40 5-cm wide bins. For each cell, the instantaneous filtered fluorescence value at each position was plotted separately for each lap to show the detailed activity of the cell throughout the entire recording session. These rate maps were used to identify place field onsets and calculate reliability within a recording session. In addition, an average rate map was created by calculating the average activity of the cell across all laps, using a smaller 3.33 cm bin (60 bins) for higher spatial resolution. These average rate maps were used to visualize population place cell activity, calculate spatial information scores, generate session-wide population vectors and calculate rate map correlations (see below). Place cell detection Cells with significant place fields were identified using two criteria: 1) the cell had to have a statistically significant spatial information score, and 2) the place field had to have a defined onset lap. The spatial information score and significance was calculated as described previously. 62 The mutual information between the mouse’s position and the cell’s activity was calculated from the average rate map of the cell. The cell’s activity and the position of the mouse were shifted randomly relative to each other and the spatial information score was calculated for the resulting rate map. This process was repeated 100 times and any cell with a spatial information score exceeding the score for >95% of shuffled maps was considered significant. To identify cells with a place field onset, 16 we first set an activity threshold defined as 15% of the difference between the most active bin and the average of the 10 least active bins of the rate map. Potential place fields were defined as any contiguous bins above this threshold and putative fields separated by a single bin of low activity were merged. Any potential fields larger than 3 bins (15 cm) with activity on at least five laps were further analyzed. The first lap that had activity within a putative field and at least one of the next five laps was defined as the place field onset. While the individual criteria for significant spatial info and for place field onset were relatively permissive, combining both methods achieved reliable place field detection while minimizing false positives. Remapping and activity measures To calculate the reliability of individual cells within a session, we calculated the Pearson’s correlation coefficient between each pair of laps with activity within a session. A cell’s reliability was defined as the mean value of the lap x lap correlation matrix. 101 Reliability was only calculated for cells with activity in at least 10% of all laps. An activity rate for each cell was defined as the area under the curve (AUC) of the filtered, normalized calcium trace, divided by the time the animal spent running (excluding pauses). As a measure of remapping, we compared the correlation values between average rate maps in the same environment (F1/F2 and N1/N2) or between different environments (F1/N1) (Fig. S3). Correlation values were only calculated if the cell had a detected place field in at the F1 or N1 session. In addition, we calculated a remapping index to compare the effects of DCZ on place cell remapping. 69 , 102 The remapping index was calculated for any cell with a detected place field in either F1 or F2, and was calculated by subtracting the average of the F1/N1 and N1/F2 rate map correlation values from the correlation between F1 and F2 average rate maps. High values indicate a cell that has stable activity within the familiar environment but remaps robustly in the novel environment. We also calculated the population overlap, which was defined as the proportion of cells with a place field in either of two sessions that had a field in both of those sessions. For the subset of cells that had a field in both sessions, we calculated the distance between the center of mass (COM) of each place field. The weighted average COM was calculated for each place field, as previously described. 16 For each cell, the absolute value of the pairwise difference was calculated between all place field COMs in the two sessions after transforming place field maps to circular coordinates (joining the start and end of the linear map). Reduced remapping should cause a reduction in the population COM drift, caused by more cells having place cells at the same location in both sessions. Rate map and population vector correlations To measure place field correlations across exposures to the same environment we identified cells with place fields in the first of the two environments (F1 for familiar, or N1 for the novel environment) and calculated the Pearson’s correlation coefficient between the mean activity rate maps in both sessions (F1/F2 or N1/N2). To calculate population vector (PV) correlations, PVs were created for each of 60 spatial bins along the track. Each PV consisted of the average activity of all cells in that bin. To compare the population similarity across exposures to the same context, the Pearson’s correlation coefficient was calculated between all pairs of PVs in the two environments (each bin in the first session was correlated to every bin in the second session). This generated the PV correlation matrices in Figs. 4A and 4E . In these plots, the x-axis represents the PV at each bin in the first session (F1 or N1) and the y-axis represents activity at each bin in the second session (F2 or N2). We focused our further analysis on PV correlation values at identical locations in both sessions (values along the main diagonal of the correlation matrix). This allowed us to quantify how similar the population representations were when spatial cues were identical. Lap-wise PV analysis To evaluate lap-by-lap PV dynamics ( Figs. 5 , 6), we first created a reference PV (PV ref ) using the average rate map across all laps in the first session within each environment (F1 for familiar, N1 for novel). Rolling average rate maps were then created using a five-lap moving window in either the same session (F1, N1) or the second session in the same environment (F2, N2). Each rolling-average PV was correlated with PV ref at corresponding locations, and the average value across all bins was used to produce a PV correlation curve for each condition. Analysis was restricted to the minimum number of laps completed by all mice on both the saline and DCZ injection day (12-15 laps across conditions). To quantify the effects of MC or GC inhibition, we calculated the mean squared error (MSE) between PV correlation curves for saline and DCZ days. The significance of the observed effect was evaluated using a shuffling procedure (permutation test). Cells recorded across both conditions were randomly assigned to either the saline or DCZ condition (assigned so that the number of cells in each condition matched the real data). PV correlation curves were created from these shuffled populations, and MSE values were calculated. This shuffling procedure was repeated 1000 times and the p value was defined as the proportion of shuffled MSE values greater than the observed MSE value. To compare the magnitude of effect sizes between novel and familiar environments, the difference between observed and shuffled MSE values was calculated for each shuffle. The difference between these values for the familiar and novel environment was taken to generate a distribution of effect size differences, with positive values indicating larger effects in the novel environment and negative values indicating larger effects in familiar ones. These analyses were also repeated after replacing the session-wide PV ref with a PV generated from the average rate map of the last five laps of the session for each cell (Fig. S9). We also calculated PV correlations between the session-wide PV ref and PVs created from either the first half or last half of all laps in each session. Unlike the rolling-average PVs above, these PVs included all laps recorded in every session. These PVs (from either the first or second session within the environment, as above) were then correlated with the session-wide PV ref from the first session. Bootstrap analysis Bootstrap effect size estimation figures were created using the DABEST (Data Analyss with Bootstrapped Estimation) package for python. 103 In these plots, 5,000 bootstrapped samples are made from the initial distribution and the differences between the mean of these bootstrapped samples are calculated. The plots show the distribution of mean differences (histogram) between these bootstrapped distributions, with the dot representing the mean value and the line representing the 95% confidence interval for the effect size. When directly comparing between two effect sizes, the mean difference of one comparison is subtracted from the mean difference of the other comparison, and this is repeated for all bootstrapped distributions to generate a new distribution of possible effect size differences (delta-delta plot). These effects can be considered significant if the bootstrapped distribution does not significantly overlap with 0, and the p values provided are the results of two-tailed permutation tests. For rate map and PV correlations, the effect of MC or GC inhibition was calculated using a bootstrapped shuffling analysis ( Figs. 2D , 3D , 4D , 4H ). To control for differences in the number of cells recorded in each animal, bootstrapped samples of 100 place cells (for rate map correlations) or 500 cells (for PV correlation) were selected from each mouse. One MC inhibition mouse was excluded from these analyses due to the low number of cells recorded. The absolute value of the difference between the mean bootstrap correlation value on the saline and DCZ days was calculated. Within the same bootstrapped sample, the mean correlation difference (absolute value) was calculated after randomly assigning each cell to the saline or DCZ day. This process was repeated 1,000 times and the mean of the bootstrapped correlation difference distribution (red line) was compared to the distribution of shuffled correlation differences (histogram). The p-value was defined as the number of shuffled mean correlation values larger than the observed mean correlation difference (absolute values). Statistics Statistical tests were calculated using MATLAB and python. Data represent mean and SEM unless otherwise stated. The p values of Wilcoxon rank-sum tests and χ 2 tests represent the results of two-tailed tests, and results were considered significant if the p value was < 0.05. Violin plots are plotted in MATLAB, with all data points overlaid. The red line represents the median value of the distribution, and kernel bandwidths were constrained to possible values (i.e. positive values for AUC, -1 to 1 for correlation values) and were kept consistent for all comparable plots. Acknowledgements This study was funded by NIH F32-NS124752 (DG), and DP2-NS111657, RO1-MH136274, RF1 NS127123, The Whitehall Foundation, The Searle Scholars Program, The Sloan Foundation, and University of Chicago startup funds (MS). We thank Reilly McClanahan and Jovangelis Gonzalez Del Toro for assistance with this project, Kimberly Christian and Antoine Madar for feedback on the manuscript, and the Tonegawa lab for providing the Dock10-cre mice. Figs. 1A, C, and D were created using BioRender. Confocal imaging was performed at the University of Chicago Integrated Light Microscopy Core RRID: SCR_019197. This manuscript is the result of funding in whole or in part by the National Institutes of Health (NIH). It is subject to the NIH Public Access Policy. 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