Abstract
Memory formation involves binding of contextual features into a unitary representation1,2,3,4, whereas memory recall can occur using partial combinations of these contextual features. The neural basis underlying the relationship between a contextual memory and its constituent features is not well understood; in particular, where features are represented in the brain and how they drive recall. Here, to gain insight into this question, we developed a behavioural task in which mice use features to recall an associated contextual memory. We performed longitudinal imaging in hippocampus as mice performed this task and identified robust representations of global context but not of individual features. To identify putative brain regions that provide feature inputs to hippocampus, we inhibited cortical afferents while imaging hippocampus during behaviour. We found that whereas inhibition of entorhinal cortex led to broad silencing of hippocampus, inhibition of prefrontal anterior cingulate led to a highly specific silencing of context neurons and deficits in feature-based recall. We next developed a preparation for simultaneous imaging of anterior cingulate and hippocampus during behaviour, which revealed robust population-level representation of features in anterior cingulate, that lag hippocampus context representations during training but dynamically reorganize to lead and target recruitment of context ensembles in hippocampus during recall. Together, we provide the first mechanistic insights into where contextual features are represented in the brain, how they emerge, and how they access long-range episodic representations to drive memory recall.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Source data are provided with this paper. All other data that supports this study are available from the corresponding author upon request.
Code availability
Source extraction codes used in this study are publicly available. The custom analysis codes are available from the corresponding author upon request.
References
O’Reilly, R. C. & Rudy, J. W. Conjunctive representations in learning and memory: Principles of cortical and hippocampal function. Psychol. Rev. 108, 311–345 (2001).
O’Keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Oxford Univ. Press, 1978).
Wood, E. R., Dudchenko, P. A. & Eichenbaum, H. The global record of memory in hippocampal neuronal activity. Nature 397, 613–616 (1999).
Schiller, D. et al. Memory and space: towards an understanding of the cognitive map. J. Neurosci. 35, 13904–13911 (2015).
Rolls, E. T. The mechanisms for pattern completion and pattern separation in the hippocampus. Front. Syst. Neurosci. 7, 74 (2013).
Nakazawa, K. et al. Requirement for hippocampal CA3 NMDA receptors in associative memory recall. Science 297, 211–218 (2002).
Wills, T. J., Lever, C., Cacucci, F., Burgess, N. & O’Keefe, J. Attractor dynamics in the hippocampal representation of the local environment. Science 308, 873–876 (2005).
Leutgeb, J. K., Leutgeb, S., Moser, M.-B. & Moser, E. I. Pattern separation in the dentate gyrus and CA3 of the hippocampus. Science 315, 961–966 (2007).
Kirwan, C. B., Wixted, J. T. & Squire, L. R. Activity in the medial temporal lobe predicts memory strength, whereas activity in the prefrontal cortex predicts recollection. J. Neurosci. 28, 10541–10548 (2008).
Eichenbaum, H., Yonelinas, A. P. & Ranganath, C. The medial temporal lobe and recognition memory. Annu. Rev. Neurosci. 30, 123–152 (2007).
Marr, D. Simple memory: a theory for archicortex. Phil. Trans. R Soc. Lond. B 262, 23–81 (1971).
Alberini, C. M. Mechanisms of memory stabilization: are consolidation and reconsolidation similar or distinct processes? Trends Neurosci. 28, 51–56 (2005).
Schwindel, C. D. & McNaughton, B. L. Hippocampal–cortical interactions and the dynamics of memory trace reactivation. Prog. Brain Res. 193, 163–177 (2011).
Duncan, J. An adaptive coding model of neural function in prefrontal cortex. Nat. Rev. Neurosci. 2, 820–829 (2001).
Roy, J. E., Riesenhuber, M., Poggio, T. & Miller, E. K. Prefrontal cortex activity during flexible categorization. J. Neurosci. 30, 8519–8528 (2010).
Eichenbaum, H., Kuperstein, M., Fagan, A. & Nagode, J. Cue-sampling and goal-approach correlates of hippocampal unit activity in rats performing an odor-discrimination task. J. Neurosci. 7, 716–732 (1987).
Kaifosh, P. & Losonczy, A. Mnemonic functions for nonlinear dendritic integration in hippocampal pyramidal circuits. Neuron 90, 622–634 (2016).
Freeman, J. et al. Mapping brain activity at scale with cluster computing. Nat. Methods 11, 941–950 (2014).
Fyhn, M., Hafting, T., Treves, A., Moser, M.-B. & Moser, E. I. Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446, 190–194 (2007).
Deshmukh, S. S. & Knierim, J. J. Influence of local objects on hippocampal representations: Landmark vectors and memory. Hippocampus 23, 253–267 (2013).
Komorowski, R. W., Manns, J. R. & Eichenbaum, H. Robust conjunctive item-place coding by hippocampal neurons parallels learning what happens where. J. Neurosci. 29, 9918–9929 (2009).
Rajasethupathy, P. et al. Projections from neocortex mediate top-down control of memory retrieval. Nature 526, 653–659 (2015).
Tomita, H., Ohbayashi, M., Nakahara, K., Hasegawa, I. & Miyashita, Y. Top-down signal from prefrontal cortex in executive control of memory retrieval. Nature 401, 699–703 (1999).
Xu, W. & Sudhof, T. C. A neural circuit for memory specificity and generalization. Science 339, 1290–1295 (2013).
Ito, H. T., Zhang, S.-J., Witter, M. P., Moser, E. I. & Moser, M.-B. A prefrontal–thalamo–hippocampal circuit for goal-directed spatial navigation. Nature 522, 50–55 (2015).
Malik, R., Schamiloglu, S. & Sohal, V. S. Top-down control of hippocampal signal-to-noise by prefrontal long-range inhibition. Cell 185, 1602–1617.e17 (2022).
Hargreaves, E. L., Rao, G., Lee, I. & Knierim, J. J. Major dissociation between medial and lateral entorhinal input to dorsal hippocampus. Science 308, 1792–1794 (2005).
Deshmukh, S. S. & Knierim, J. J. Representation of non-spatial and spatial information in the lateral entorhinal cortex. Front. Behav. Neurosci. 5, 69 (2011).
Basu, J. et al. Gating of hippocampal activity, plasticity, and memory by entorhinal cortex long-range inhibition. Science 351, aaa5694 (2016).
Woods, N. I. et al. The dentate gyrus classifies cortical representations of learned stimuli. Neuron 107, 173–184.e6 (2020).
Mahn, M. et al. High-efficiency optogenetic silencing with soma-targeted anion-conducting channelrhodopsins. Nat. Commun. 9, 4125 (2018).
Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
Raposo, D., Kaufman, M. T. & Churchland, A. K. A category-free neural population supports evolving demands during decision-making. Nat. Neurosci. 17, 1784–1792 (2014).
DeNardo, L. A. et al. Temporal evolution of cortical ensembles promoting remote memory retrieval. Nat. Neurosci. 22, 460–469 (2019).
Sofroniew, N. J., Flickinger, D., King, J. & Svoboda, K. A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging. eLife 5, e14472 (2016).
Wagner, A. D. et al. Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science 281, 1188–1191 (1998). Aug 21.
Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).
Koechlin, E., Ody, C. & Kouneiher, F. The architecture of cognitive control in the human prefrontal cortex. Science 302, 1181–1185 (2003).
Guise, K. G. & Shapiro, M. L. Medial prefrontal cortex reduces memory interference by modifying hippocampal encoding. Neuron 94, 183–192.e8 (2017).
Jo, Y. S. et al. The medial prefrontal cortex is involved in spatial memory retrieval under partial-cue conditions. J. Neurosci. 27, 13567–13578 (2007).
Yu, J. Y., Liu, D. F., Loback, A., Grossrubatscher, I. & Frank, L. M. Specific hippocampal representations are linked to generalized cortical representations in memory. Nat. Commun. 9, 2209 (2018).
Bernardi, S. et al. The geometry of abstraction in the hippocampus and prefrontal cortex. Cell 183, 954–967.e21 (2020).
Reinert, S., Hübener, M., Bonhoeffer, T. & Goltstein, P. M. Mouse prefrontal cortex represents learned rules for categorization. Nature 593, 411–417 (2021).
Beck, J. M. et al. Probabilistic population codes for Bayesian decision making. Neuron 60, 1142–1152 (2008).
Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M. & Harris, K. D. High-dimensional geometry of population responses in visual cortex. Nature 571, 361–365 (2019).
Yoon, K. et al. Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Nat. Neurosci. 16, 1077–1084 (2013).
Zhao, X., Wang, Y., Spruston, N. & Magee, J. C. Membrane potential dynamics underlying context-dependent sensory responses in the hippocampus. Nat. Neurosci. 23, 881–891 (2020).
Sargolini, F. Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science 312, 758–762 (2006).
Ziv, Y. et al. Long-term dynamics of CA1 hippocampal place codes. Nat. Neurosci. 16, 264–266 (2013).
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N. & Harvey, C. D. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 170, 986–999.e16 (2017).
Richards, B. & Frankland, P. The persistence and transience of memory. Neuron 94, 1071–1084 (2017).
Kitamura, T. et al. Engrams and circuits crucial for systems consolidation of a memory. Science 356, 73–78 (2017).
Zingg, B et al. AAV-mediated anterograde transsynaptic tagging: mapping corticocollicular input-defined neural pathways for defense behaviors. Neuron 93,33–47 (2017).
Dombeck, D. A., Harvey, C. D., Tian, L., Looger, L. L. & Tank, D. W. Functional imaging of hippocampal place cells at cellular resolution during virtual navigation. Nat. Neurosci. 13, 1433–1440 (2010).
Harvey, C. D., Coen, P. & Tank, D. W. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012).
Niell, C. M. & Stryker, M. P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472–479 (2010).
Chettih, S. N. & Harvey, C. D. Single-neuron perturbations reveal feature-specific competition in V1. Nature 567, 334–340 (2019).
Guenthner, C. J., Miyamichi, K., Yang, H. H., Heller, H. C. & Luo, L. Permanent genetic access to transiently active neurons via TRAP: targeted recombination in active populations. Neuron 78, 773–784 (2013).
Ye, L. et al. Wiring and molecular features of prefrontal ensembles representing distinct experiences. Cell. 165, 1776–1788 (2016).
Pnevmatikakis, E. A. & Giovannucci, A. NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data. J. Neurosci. Methods 291, 83–94 (2017).
Sheintuch, L. et al. Tracking the same neurons across multiple days in Ca2+ imaging data. Cell Rep. 21, 1102–1115 (2017).
Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv https://doi.org/10.1101/061507 (2016).
Cossell, L. et al. Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518, 399–403 (2015).
Ko, H. et al. Functional specificity of local synaptic connections in neocortical networks. Nature 473, 87–91 (2011).
Acknowledgements
We thank T. Wiesel, S. Siegelbaum, A.-L. Kumar, S. Sankaran, V. Ruta and S. Deshmukh for helpful discussions or critical reading of the manuscript; F. Hollunder and M. Gebremedhin for their technical assistance; R. Shao in the laboratory of J. Friedman, and S. Johnson and G. Nieves in the laboratory of C.L. for generously providing FosTRAP2 mice. This work is supported by a Kavli Neuroscience Institute pilot grant from the Rockefeller University, the Harold & Leila Mathers Foundation, Halis Family Foundation, Searle Foundation and the National Institutes of Health under award number R00MH109652.
Author information
Authors and Affiliations
Contributions
N.Y. and P.R. conceived the study and designed the experiments. N.Y. and J.E.N. developed and characterized the VR-based behavioural task. N.Y. performed imaging in hippocampus, anterior cingulate cortex (together with J.E.N.), paired inhibition and imaging experiments (together with C.N.), and dual imaging experiments (together with C.N.). N.Y. performed data analyses with supervision from J.V., C.L. and P.R. A.T. assisted with optogenetics experiments. N.Y. and P.R. wrote the paper with input from all authors.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature thanks Vikaas Sohal and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Behavioral performance during training and retrieval.
a, Latency to the first lick in reward (red), neutral (blue) and aversive (black) context in both, reinforced and probe trials during training. n = 12 mice, 24 sessions. (Two-way ANOVA with Sidak’s post hoc; adjusted *p = 0.029; ***p = 0.001; ****p < 0.0001) b, Lick rate modulation in full cue (AVOT) trials during retrieval (n = 12 mice, 18 sessions; Two-way ANOVA with Sidak’s multiple comparisons; adjusted ***p < 0.005). c, Histology of bilateral inhibition of CA1 throughout training (T1–T3, reinforced trials only, not probe trials) using st-GtACR2 and cannula implant (right, Scale: 1000μm); behavioral performance measured as discrimination index (reward– aversive context lick rate / total lick rate) on probe trials in control (mCherry) vs. opsin (GtACR) cohorts (n = 6 each; Two-way ANOVA with Sidak’s multiple correction, adjusted *p = 0.015, Data are mean±s.e.m) d-e, GRIN lens implant does not affect learning d, lick rate modulation of mice implanted with GRINs in reinforced and probe trials, n = 16 mice, (Two-way ANOVA performed with Sidak’s multiple comparisons, p<adjusted *p < 0.05, ***p < 0.0105, ****p < 0.001) e, Discrimination index in reinforced and probe trials over training days 1–3 (T1–T3); n = 16 mice, Two-way ANOVA with Sidak’s multiple comparison test, adjusted *p = 0.01, Data are mean±s.e.m). Details of statistical analyses in Supplementary Table
Extended Data Fig. 2 Two-photon imaging in CA1 and extraction of neural sources and activity.
a, Mean intensity Z-projections of two-photon-acquired imaging videos in CA1, showing 3 z-planes spaced 60um apart. Scale: 50 μm, b, Example GCaMP6f neural traces during behavior with identified transients overlaid on raw dF/F activity in c. d, Identification of significant transients in dF/F traces: Histograms show distribution of positive and negative events above 2 σ thresholds over range of durations; negative going transients (red) compared to positive going transients (blue). Similar analysis for 3 σ and 4 σ threshold in e-f; g, False positive rate of transients as a function of time. FPR is described as ratio of negative to positive transients for each duration pooled across all mice (n = 3 mice, 11 sessions) and fields of view and sessions (Data are mean±s.e.m). False positive curve (bottom right) is fit to an exponential curve to determine minimum transient duration and σ threshold for FPR < 5%. Event onset was then described as transient going above 2 σ thresholds for 2 frames (~0.6 s) h, Chance probability of a neuron to be categorized as “feature-responsive” as a function of fraction of trials that neuron is active (n = 3 mice, 9 sessions in training, 9 in retrieval). If a neuron is active on 40% of trials for a given feature, the probability of being false positive is < 0.05. Data is presented as mean±sem i, %neurons classified as “feature-responsive” on retrieval trials using criteria set in h. Details of statistical analyses in Supplementary Table
Extended Data Fig. 3 Context discrimination in CA1.
a–d, Single neurons registered across training days 2 and 3 (T2-T3) and retrieval days 1 and 2 (R1-R2). a, dF/F activity of neuron aligned to start of reward context probe trials (red) and aversive context probe trials (black) shows acquisition of aversive context selectivity from training day2 (top) to training day 3 (bottom), with stable context responses during aversive feature presentations in retrieval days 1-2, shown in b. c-d, same as a-b for reward context selectivity. e, Above, Heatmap shows dF/F responses of reward selective neurons in reward probe trials (left) and aversive probe trials (right) aligned to context onset at t = 0 and context end at t = 10s (white dashed lines). Below, similar heatmap shown for aversive selective neurons in reward and aversive probe trials. f, Neural population trajectories on probe trials, similar to Fig. 1h but during training, showing divergent population activity in reward (red) and aversive (black) probe trials, with variance explained by first 3 PC’s (m1: 29%, m2: 19%) g, Performance of a linear SVM decoder trained on dF/F responses at each time point after context entry in reward and aversive reinforced trials and tested on probe trials shows population level discrimination between reward and aversive contexts. (n = 3 mice, 6 sessions (training). Data is presented as mean ± sem, dashed line shows chance at y = 0.5), h, Quantification of the fraction of neurons that are context-selective (in training), feature-selective or conjunctive (in retrieval); data points represent individual mice (n = 3 mice, *p = 0.024; paired t-test). Details of statistical analyses in Supplementary Table
Extended Data Fig. 4 CA1 context-selective neurons are conjunctive and not movement related.
a-b, sample traces of 3 context selective neurons (z-scored, orange) overlaid with lick rate (blue, in a) and speed (blue, in b), showing no significant correlation to motor activity (black dashed lines indicate onset of contexts). Context selective neurons are distributed evenly in the field of view (red: reward ensemble, black: aversive ensemble cell centroids) with low correlations (z axis) to lick rates (c), speed (d) and acceleration (e), shown here for a representative mouse. f, feature responsive ensembles exhibit highly generalized activity across all features of the same context, as shown in (g) (Scale; x:1s, y:0.2dF/F). g, Single trial neural trajectories show indiscernible feature trajectories of the same context (similar to Fig. 1h but all trials projected), but divergent across the two contexts (shade of red are reward context feature trials, shades of black are aversive context feature trials) for 3 mice, with variance explained by first 3 PC’s (34%, 28%, 25%)
Extended Data Fig. 5 Retrograde tracing from dHPC and histology.
a, Schematic of retrograde tracing from dHPC by injecting rgAAV-CAG-tdT in CA1 & CA3. Detection of retrogradely labeled tdT neurons (in red) together with nuclei stained with DAPI (blue) from coronal sections at 10x; insets show zoom at 40x; CLA: claustrum, MS: Medial septum; LSr: Lateral Septum, AC: Anterior Cingulate, TH – Thalamus; HY/fx: Hypothalamus/fornix, BLA: Basolateral Amygdala, LEC: Lateral Entorhinal Cortex (Scale: 1000 μm) b, Schematic and histology for inhibition imaging. St-GtACR2 (stained with DAPI) in AC/LEC and hSyn-GCaMP6f in CA1. Dotted line denotes placement of GRIN lens. Scale:400 μm.
Extended Data Fig. 6 CA1 activity in the presence and absence of opsin.
a-d, No significant activation of st-GtACR2 in AC/LEC during two-photon imaging in CA1 as assessed by similar neural activity patterns with (AC-GtACR, LEC GtACR) and without (ctrl) opsin expression. a–c, Mean event rate, time between consecutive events, and mean onset time of CA1 neurons across all three cohorts (ctrl no opsin, n = 4; AC st-GtACR2, n = 4; LEC st-GtACR2, n = 3) with no significant differences. d, Fraction of CA1 neurons that are context selective is similar across cohorts (ctrl no opsin, n = 4 mice,9 sessions; AC st-GtACR2, n = 3 mice, 7 sessions; LEC st-GtACR2, n = 3 mice, 6 sessions) e-f, Mean ensemble onset activity of CA1 neurons across both AC st-GtACR and LEC st-GtACR cohorts during light-off trials (no optical inhibition) is similar across cohorts for both context-selective neurons (top) and non-context selective neurons (bottom) (All data are mean mean±s.e.m) g, Mean onset activity in CA1 during light off and light on trials in context selective ensemble (top, Two way ANOVA with Sidak’s correction, adjusted p values for AVOT *** p < 0.0001, OT ****p < 0.0001) and non-context ensemble (bottom, Two way ANOVA with Sidak’s correction, adjusted p values for AVOT p = 0.008, AOT p = 0.0003, OT p = 0.0001) for LEC inhibition cohort, signifies widespread inhibition of neurons in CA1 (n = 3 mice, 6 sessions). h, same as in (g) but for AC inhibition cohort (n = 4 mice, 7 sessions), context neurons (top, Two way ANOVA with Sidak’s correction, adjusted p values for AVOT p = 0.009, AOT p < 0.0001, OT p = 0.0001) and non-context neurons (bottom, n.s.). i, Percent of all recorded CA1 (top) and CA1 context neurons (bottom) that were inhibited during AC/LEC optical inhibition (n = 4 mice, AC inhibition; n = 3 mice, LEC inhibition; Students t-test p < 0.05 for CA1 context neurons; mean, quartile, minimum and maximum are shown). j, Percent of CA1 context and non-context neurons inhibited during AC/LEC optical inhibition for all trials types (AVOT, AOT and OT) combined for reward and aversive trials, n = 4 mice, AC inhibition; n = 3 mice, LEC inhibition, each data point represents an individual mouse (context vs non-context neurons; for AC, F(1,18) = 36.39, p < 0.001; for LEC; F(1,12) = 1.749, p = 0.21; Two-way ANOVA with Sidak’s multiple comparison) k, Percent inhibition of dF/F activity of context vs non-context neuron ensembles across all trial types (AVOT, AOT and OT) combined for aversive and reward trials, n = 3 mice, 6 sessions for LEC (left), 4 mice, 7 sessions for AC (right), data are mean with each session as an individual data point; (Two-way ANOVA with Sidak’s multiple comparison test; p = 0.035, adjusted *p < 0.05; **p < 0.01). Details of statistical analyses in Supplementary Table
Extended Data Fig. 7 AC and LEC project to non-overlapping subsets of neurons in CA1.
a, Anterograde tracing from AC (AAV1-EF1a-Flp) and LEC (AAV1-hSyn-Cre) and a mixture of AAV1-CAG-GIO GFP and AAV1-EF1a-fDIO-mCherry in CA1. Neurons in red receive inputs from AC and green receive inputs from LEC, with absence of neurons having convergent input from AC and LEC (Scale: 400 μm (left), 200 μm(right)). b, Same as in (a) but injected with AAV1-hSyn-Cre in AC and AAV1-EF1a-Flp in LEC, shows lack of neurons in CA1 with convergent inputs from both AC and LEC (Scale: 400μm (left), 400μm (right)). c, (Top) Schematic of a multiplexed construct that allows a marker to be expressed only in cells that express both Cre and Flp: injecting AAV1-hSyn-Cre in AC, and AAV1-EF1a-Flp in LEC, and AAV8-EF1a-Con/Fon-eYFP in CA1. (Bottom) Coronal section of CA1 shows no neurons receive inputs from both AC and LEC. (Scale:400 μm). d, (Left) Anterograde tracing using AAV1-hSyn-Cre, showing starter cells (stained with Cre antibody) in AC (left) or LEC (middle, overlaid with DAPI, Scale 400μm) with AC- (top) or LEC- receiving (bottom) CA1 neuron overlaid with cFos stain (magenta) after retrieval day 1 (right, Scale:40um). e, Quantification of % of CA1 neurons receiving AC/LEC inputs that are cFos positive. LEC/AC n = 4 mice, 8 slices each. p = 0.002, Welch’s t-test. Data points are individual slices, with mean±s.e.m. f, Retrograde tracing from CA1 (AAVrg-hSyn-Cre in dCA1 and AAV1-CAG-Flex-eGFP in AC) shows AC neurons (green) that project to dCA1 (Scale: 400μm). g–i, Retrograde tracing in emx1-cre (g) and vglut-cre (h) mice by injecting AAVrg-Flex-tdT in starter cells of dCA1(i) revealing excitatory AC-CA1 projections. CA1 projecting AC neurons (red) overlaid with DAPI (blue, left) and CaMKII stain (green, middle) (Scale: 400μm) and zoomed in (right, Scale: 100μm)
Extended Data Fig. 8 Two-photon imaging in AC during behavior.
a, Mean intensity Z-projections of two-photon-acquired imaging videos in AC, showing 3 z-planes spaced 60μm apart (Scale: 50μm). b, Example GCaMP6f traces during behavior. c–d, Detecting significant transients, with false positive rate of transients as a function of time, fit to an exponential curve to determine minimum transient duration and σ threshold for FPR < 5%. Event onset was then described as transient going above 2 σ thresholds for 2 frames (~0.6 s). (n = 7 mice, 14 session; Data are mean±s.e.m). Detected transients overlaid with raw traces in d. e, Retrogradely labelled CA1 projecting neurons in AC (red) in recording FOV with syn-GCaMP6f (green), indicating the FOV has direct monosynaptic access to CA1/3 (Scale: 150μm). f–h, Neuron centroids across x-y axis plotted as a function of correlations to lick rate (f), speed (g) and acceleration (h) shows minimal motor related signals in recording FOV. i, Single trial neural trajectories from representative mice (n = 2 shown) show divergence between reward and aversive trajectories in probe trials, with a linear SVM decoder trained on reinforcement trials and tested on probe trials in j, indicating contextual discrimination in AC at population level (n = 7 mice, 13 sessions in training; Data are mean±s.e.m). k, Quantification of percent of neurons responsive to each feature type across the retrieval sessions (n = 7 mice, 11 sessions in retrieval). l, Quantification of the fraction of neurons that are context-selective (in training), feature-selective or conjunctive (in retrieval); data point represents individual mouse (n = 7 mice; adjusted *p = 0.015; paired t-test). m, Schematic of bilateral Gi-DREADD-inhibition in CA1 only across training days 1–3 while performing two-pho- ton imaging in AC during training day 3 and retrieval, showing n, performance of SVM to decode context during training (n = 3 mice, P < 0.01 Mann Whitney U Test) and o, quantification of percent of AC feature responsive neurons during retrieval for CNO injected DREADD (hM4Di) vs. control (mCherry) mice (n = 3 mice, n.s. paired t-test). Data are mean±s.e.m
Extended Data Fig. 9 Feature ensembles in AC compared with context ensembles in CA1.
a, Feature responsive ensembles in AC respond minimally to other features in the same context, shown for reward (left) and aversive (right) feature presentations. b, Quantification of net dF/F activity of feature responsive neurons to all other feature presentations in AC (left) and CA1 (right) from n = 3 mice in reward context. Note: only context responsive neurons are shown (not all recorded neurons). Data are presented either as mean±s.e.m (above) or as heatmap (below), with adjacent column indicating whether the neuron was statistically classified as feature selective (white) or not (black). See Fig. 3f for aversive c, Quantification of net relative response of a feature selective ensemble to other features of the same context (dark) versus the opposite context (light) in AC and CA1 respectively across all mice, each point represents individual mouse (n = 7 mice (t16 = 2.849; *p = 0.023); n = 3 mice CA1 (t16 = 5.035; ***p = 0.0002); Two-way ANOVA with Sidak’s multiple comparison test). d, Performance of SVM to decode reward and aversive trials with all features pooled into either context (n = 3 mice, 9 sessions CA1; 4 mice, 11 sessions AC; Data are mean±s.e.m). e, Schematic of state-space location of different features in an N-dimensional space (N is the number of neurons) and defining separation index as ratio of inter-contextual to intra-contextual distance (right). Separation index for AC (n = 12 sessions) and CA1 (n = 9 sessions) (**p = 0.003; Student’s t-test; data as mean±s.e.m) (left). f, Schematic of FOSTRAP behavioral paradigm. Coronal section of AC shows neurons expressing st-GtACR2 (red) stained with DAPI (blue) with cannula implant (Scale: 500μm, 100μm). Details of statistical analyses in Supplementary Table
Extended Data Fig. 10 Simultaneous imaging of AC and CA1 during behavior.
a, Histology of AC and CA1 GRIN lens implant from a representative mouse. Scale = 500μm. b, Anatomical location of each recorded neuron and its activity correlations with motor variables (speed, acceleration) in AC (top) and CA1 (bottom) c, Behavioral performance of dual GRIN implanted and recorded mice, shown as average lick rate on retrieval day 1 (R1) across aversive and neutral features on modified one-day behavioral paradigm (n = 3 mice, *p < 0.023; paired t-test). d, Left: Average fraction of context selective neurons in AC and CA1 during training day T1, with average ensemble size of feature responsive neurons in these regions during retrieval session R1 (right). e, Location of context-selective neurons are evenly distributed throughout field of view in both AC and CA1. f, Top: Proportion of context selective neurons responding (cumulative distribution function) to context onset during training for two mice as a function of latency (Kolmogorov–Smirnov two-tail test, p = 0.015 (m2), p = 0.001 (m3)) (purple-CA1; green-AC), with mean onset time for AC and CA1 (n = 3, paired t-test p < 0.05) Bottom: Same but for retrieval, with feature selective neurons in AC and context selective neurons in CA1 (Kolmogorov–Smirnov two-tail test, p = 0.002(m2), p < 0.0001 (m3)), with mean onset times of AC and CA1 (n = 3 mice, paired t-test, p = 0.01 (Training); p < 0.0001 (Retrieval)). g, proportion of shock responsive neurons active after context-onset (showing first and last trials) as a function of time (cumulative distribution function), and shown separately for each individual mouse. Details of statistical analyses in Supplementary Table
Supplementary information
Supplementary Information
This file contains Supplementary Note 1, legend to Supplementary Video 1 and references.
Supplementary Table
Summary Statistics.
Source data
Rights and permissions
About this article
Cite this article
Yadav, N., Noble, C., Niemeyer, J.E. et al. Prefrontal feature representations drive memory recall. Nature 608, 153–160 (2022). https://doi.org/10.1038/s41586-022-04936-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-022-04936-2
This article is cited by
-
Sex differences in learning and performing the Go/NoGo tasks
Biology of Sex Differences (2023)
-
Thalamic nucleus reuniens coordinates prefrontal-hippocampal synchrony to suppress extinguished fear
Nature Communications (2023)
-
Engram neurons: Encoding, consolidation, retrieval, and forgetting of memory
Molecular Psychiatry (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
