Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Projections from neocortex mediate top-down control of memory retrieval

Abstract

Top-down prefrontal cortex inputs to the hippocampus have been hypothesized to be important in memory consolidation, retrieval, and the pathophysiology of major psychiatric diseases; however, no such direct projections have been identified and functionally described. Here we report the discovery of a monosynaptic prefrontal cortex (predominantly anterior cingulate) to hippocampus (CA3 to CA1 region) projection in mice, and find that optogenetic manipulation of this projection (here termed AC–CA) is capable of eliciting contextual memory retrieval. To explore the network mechanisms of this process, we developed and applied tools to observe cellular-resolution neural activity in the hippocampus while stimulating AC–CA projections during memory retrieval in mice behaving in virtual-reality environments. Using this approach, we found that learning drives the emergence of a sparse class of neurons in CA2/CA3 that are highly correlated with the local network and that lead synchronous population activity events; these neurons are then preferentially recruited by the AC–CA projection during memory retrieval. These findings reveal a sparsely implemented memory retrieval mechanism in the hippocampus that operates via direct top-down prefrontal input, with implications for the patterning and storage of salient memory representations.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Characterization of AC–CA monosynaptic projection.
Figure 2: AC–CA projections control top-down memory retrieval.
Figure 3: Memory formation generates highly correlated HC neurons that represent context.
Figure 4: The AC–CA projection preferentially recruits HC neurons during memory retrieval.

References

  1. Kandel, E. R., Dudai, Y. & Mayford, M. R. The molecular and systems biology of memory. Cell 157, 163–186 (2014)

    CAS  PubMed  Google Scholar 

  2. Han, J. H. et al. Neuronal competition and selection during memory formation. Science 316, 457–460 (2007)

    CAS  PubMed  ADS  Google Scholar 

  3. Han, J. H. et al. Selective erasure of a fear memory. Science 323, 1492–1496 (2009)

    CAS  PubMed  ADS  Google Scholar 

  4. Yiu, A. P. et al. Neurons are recruited to a memory trace based on relative neuronal excitability immediately before training. Neuron 83, 722–735 (2014)

    CAS  PubMed  Google Scholar 

  5. Reijmers, L. G., Perkins, B. L., Matsuo, N. & Mayford, M. Localization of a stable neural correlate of associative memory. Science 317, 1230–1233 (2007)

    CAS  ADS  PubMed  Google Scholar 

  6. Liu, X. et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484, 381–385 (2012)

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

  7. Garner, A. R. et al. Generation of a synthetic memory trace. Science 335, 1513–1516 (2012)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  8. Ramirez, S. et al. Creating a false memory in the hippocampus. Science 341, 387–391 (2013)

    CAS  ADS  PubMed  Google Scholar 

  9. Tse, D. et al. Schema-dependent gene activation and memory encoding in neocortex. Science 333, 891–895 (2011)

    CAS  PubMed  ADS  Google Scholar 

  10. Lesburguères, E. et al. Early tagging of cortical networks is required for the formation of enduring associative memory. Science 331, 924–928 (2011)

    PubMed  ADS  Google Scholar 

  11. Bero, A. W. et al. Early remodeling of the neocortex upon episodic memory encoding. Proc. Natl Acad. Sci. USA 111, 11852–11857 (2014)

    CAS  PubMed  ADS  PubMed Central  Google Scholar 

  12. Frankland, P. W. & Bontempi, B. The organization of recent and remote memories. Nature Rev. Neurosci. 6, 119–130 (2005)

    CAS  Google Scholar 

  13. Ressler, K. J. & Mayberg, H. S. Targeting abnormal neural circuits in mood and anxiety disorders: from the laboratory to the clinic. Nature Neurosci. 10, 1116–1124 (2007)

    CAS  PubMed  Google Scholar 

  14. Taylor, S. F. et al. Meta-analysis of functional neuroimaging studies of emotion perception and experience in schizophrenia. Biol. Psychiatry 71, 136–145 (2012)

    PubMed  Google Scholar 

  15. Wilson, S. J., Sayette, M. A. & Fiez, J. A. Prefrontal responses to drug cues: a neurocognitive analysis. Nature Neurosci. 7, 211–214 (2004)

    PubMed  Google Scholar 

  16. Nadel, L. & Moscovitch, M. Memory consolidation, retrograde amnesia and the hippocampal complex. Curr. Opin. Neurobiol. 7, 217–227 (1997)

    CAS  PubMed  Google Scholar 

  17. Winocur, G., Moscovitch, M. & Bontempi, B. Memory formation and long-term retention in humans and animals: convergence towards a transformation account of hippocampal-neocortical interactions. Neuropsychologia 48, 2339–2356 (2010)

    PubMed  Google Scholar 

  18. Strogatz, S. H. Exploring complex networks. Nature 410, 268–276 (2001)

    CAS  PubMed  MATH  ADS  Google Scholar 

  19. Barabási, A. L. & Oltvai, Z. N. Network biology: understanding the cell’s functional organization. Nature Rev. Genet. 5, 101–113 (2004)

    PubMed  Google Scholar 

  20. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Rev. Neurosci. 10, 186–198 (2009)

    CAS  Google Scholar 

  21. Hermundstad, A. M., Brown, K. S., Bassett, D. S. & Carlson, J. M. Learning, memory, and the role of neural network architecture. PLOS Comput. Biol. 7, e1002063 (2011)

    MathSciNet  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  22. Wickersham, I. R., Finke, S., Conzelmann, K. K. & Callaway, E. M. Retrograde neuronal tracing with a deletion-mutant rabies virus. Nature Methods 4, 47–49 (2007)

    CAS  PubMed  Google Scholar 

  23. Soudais, C., Laplace-Builhe, C., Kissa, K. & Kremer, E. J. Preferential transduction of neurons by canine adenovirus vectors and their efficient retrograde transport in vivo. FASEB J. 15, 2283–2285 (2001)

    CAS  PubMed  Google Scholar 

  24. 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. Nature Neurosci. 13, 1433–1440 (2010)

    CAS  PubMed  Google Scholar 

  25. Mahoney, W. J. & Ayres, J. J. B. One-trial simultaneous and backward fear conditioning as reflected in conditioned suppression of licking in rats. Anim. Learn. Behav. 4, 357–362 (1976)

    Google Scholar 

  26. Bouton, M. E. & Bolles, R. C. Conditioned fear assessed by freezing and by the suppression of three different baselines. Anim. Learn. Behav. 8, 429–434 (1980)

    Google Scholar 

  27. Lovett-Barron, M. et al. Dendritic inhibition in the hippocampus supports fear learning. Science 343, 857–863 (2014)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  28. Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  29. Cheng, S. & Frank, L. M. New experiences enhance coordinated neural activity in the hippocampus. Neuron 57, 303–313 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Komiyama, T. et al. Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice. Nature 464, 1182–1186 (2010)

    CAS  PubMed  ADS  Google Scholar 

  31. Modi, M. N., Dhawale, A. K. & Bhalla, U. S. CA1 cell activity sequences emerge after reorganization of network correlation structure during associative learning. eLife 3, e01982 (2014)

    PubMed  PubMed Central  Google Scholar 

  32. Vogelstein, J. T. et al. Fast nonnegative deconvolution for spike train inference from population calcium imaging. J. Neurophysiol. 104, 3691–3704 (2010)

    PubMed  PubMed Central  Google Scholar 

  33. Yizhar, O. et al. Neocortical excitation/inhibition balance in information processing and social dysfunction. Nature 477, 171–178 (2011)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  34. Lin, J. Y., Knutsen, P. M., Muller, A., Kleinfeld, D. & Tsien, R. Y. ReaChR: a red-shifted variant of channelrhodopsin enables deep transcranial optogenetic excitation. Nature Neurosci. 16, 1499–1508 (2013)

    CAS  PubMed  Google Scholar 

  35. Gunaydin, L. A. et al. Ultrafast optogenetic control. Nature Neurosci. 13, 387–392 (2010)

    CAS  PubMed  Google Scholar 

  36. Goshen, I. et al. Dynamics of retrieval strategies for remote memories. Cell 147, 678–689 (2011)

    CAS  PubMed  Google Scholar 

  37. Vinje, W. E. & Gallant, J. L. Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287, 1273–1276 (2000)

    CAS  PubMed  ADS  Google Scholar 

  38. Olshausen, B. A. & Field, D. J. Sparse coding of sensory inputs. Curr. Opin. Neurobiol. 14, 481–487 (2004)

    CAS  PubMed  Google Scholar 

  39. Yassin, L. et al. An embedded subnetwork of highly active neurons in the neocortex. Neuron 68, 1043–1050 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Gdalyahu, A. et al. Associative fear learning enhances sparse network coding in primary sensory cortex. Neuron 75, 121–132 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Buzsáki, G., Geisler, C., Henze, D. A. & Wang, X.-J. Interneuron Diversity series: circuit complexity and axon wiring economy of cortical interneurons. Trends Neurosci. 27, 186–193 (2004)

    PubMed  Google Scholar 

  42. Perin, R., Berger, T. K. & Markram, H. A synaptic organizing principle for cortical neuronal groups. Proc. Natl Acad. Sci. USA 108, 5419–5424 (2011)

    CAS  PubMed  ADS  PubMed Central  Google Scholar 

  43. Bonifazi, P. et al. GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science 326, 1419–1424 (2009)

    CAS  PubMed  ADS  Google Scholar 

  44. Brecht, M., Schneider, M., Sakmann, B. & Margrie, T. W. Whisker movements evoked by stimulation of single pyramidal cells in rat motor cortex. Nature 427, 704–710 (2004)

    CAS  PubMed  ADS  Google Scholar 

  45. Houweling, A. R. & Brecht, M. Behavioural report of single neuron stimulation in somatosensory cortex. Nature 451, 65–68 (2008)

    CAS  PubMed  ADS  Google Scholar 

  46. Li, C. Y., Poo, M.-M. & Dan, Y. Burst spiking of a single cortical neuron modifies global brain state. Science 324, 643–646 (2009)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  47. Prakash, R. et al. Two-photon optogenetic toolbox for fast inhibition, excitation and bistable modulation. Nature Methods 9, 1171–1179 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Rickgauer, J. P., Deisseroth, K. & Tank, D. W. Simultaneous cellular-resolution optical perturbation and imaging of place cell firing fields. Nature Neurosci. 17, 1816–1824 (2014)

    CAS  PubMed  Google Scholar 

  49. Packer, A. M., Russell, L. E., Dalgleish, H. W. & Häusser, M. Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo. Nature Methods 12, 140–146 (2015)

    CAS  PubMed  Google Scholar 

  50. Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  51. 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)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  52. Niell, C. M. & Stryker, M. P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472–479 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Mukamel, E. A., Nimmerjahn, A. & Schnitzer, M. J. Automated analysis of cellular signals from large-scale calcium imaging data. Neuron 63, 747–760 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986)

    CAS  PubMed  Google Scholar 

  55. Ko, H. et al. Functional specificity of local synaptic connections in neocortical networks. Nature 473, 87–91 (2011)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  56. Cossell, L. et al. Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518, 399–403 (2015)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  57. Tarjan, R. E. Depth first search and linear graph algorithms. SIAM J. Comput. 1, 146–160 (1972)

    MathSciNet  MATH  Google Scholar 

  58. Sedgewick, R. Algorithms in C++, Part 5 Graph Algorithms (Addison-Wesley, 2002)

    Google Scholar 

  59. Schwartz, T. H. et al. Networks of coactive neurons in developing layer 1. Neuron 20, 541–552 (1998)

    CAS  PubMed  Google Scholar 

  60. Greenberg, D. S., Houweling, A. R. & Kerr, J. N. Population imaging of ongoing neuronal activity in the visual cortex of awake rats. Nature Neurosci. 11, 749–751 (2008)

    CAS  PubMed  Google Scholar 

  61. Holekamp, T. F., Turaga, D. & Holy, T. E. Fast three-dimensional fluorescence imaging of activity in neural populations by objective-coupled planar illumination microscopy. Neuron 57, 661–672 (2008)

    CAS  PubMed  Google Scholar 

  62. Smith, S. L. & Häusser, M. Parallel processing of visual space by neighboring neurons in mouse visual cortex. Nature Neurosci. 13, 1144–1149 (2010)

    CAS  PubMed  Google Scholar 

  63. Castillo, P. E., Younts, T. J., Chávez, A. E. & Hashimotodani, Y. Endocannabinoid signaling and synaptic function. Neuron 76, 70–81 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Berndt, A., Lee, S. Y., Ramakrishnan, C. & Deisseroth, K. Structure-guided transformation of channelrhodopsin into a light-activated chloride channel. Science 344, 420–424 (2014)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  65. Lee, S. Y., Földy, C., Szabadics, J. & Soltesz, I. Cell-type-specific CCK2 receptor signaling underlies the cholecystokinin-mediated selective excitation of hippocampal parvalbumin-positive fast-spiking basket cells. J. Neurosci. 31, 10993–11002 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Kohara, K. et al. Cell type-specific genetic and optogenetic tools reveal hippocampal CA2 circuits. Nature Neurosci. 17, 269–279 (2014)

    CAS  PubMed  Google Scholar 

  67. Varga, C., Lee, S. Y. & Soltesz, I. Target-selective GABAergic control of entorhinal cortex output. Nature Neurosci. 13, 822–824 (2010)

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank B. K. Lim for the rabies virus and S. Pak for assistance with histology. We thank the entire Deisseroth laboratory for thoughtful comments, with particular gratitude to M. Lovett-Baron, A. Andalman and W. Allen for their helpful discussions throughout. K.D. is supported by the Defense Advanced Research Projects Agency Neuro-FAST program, National Institute of Mental Health, National Institute on Drug Abuse, National Science Foundation, the Simons Foundation, the Tarlton Foundation, the Wiegers Family Fund, the Nancy and James Grosfeld Foundation, the H.L. Snyder Medical Foundation, and the Samuel and Betsy Reeves Fund. This work is supported by an Ellison Life Sciences Research Foundation (LSRF) fellowship (P.R.), a Simons LSRF fellowship (J.H.M.), the German Academic Exchange Service DAAD (A.B.) and the Fidelity Foundation (S.Y.L.). All tools and methods are distributed and supported freely (http://www.optogenetics.org).

Author information

Authors and Affiliations

Authors

Contributions

P.R. and K.D. designed the experiments. P.R. performed anatomical tracing, optogenetic behaviour, virtual reality behaviour, hippocampal cranial window surgeries and in vivo imaging experiments, and collected all associated data. S.S. wrote custom code to extract neural sources and performed computational analysis on all of the in vivo calcium imaging datasets. P.R. and J.H.M. collected data from simultaneous one-photon stimulation and two-photon imaging experiments, and J.H.M. and S.S. analysed those data. E.F. and S.Y.L. performed patch electrophysiology experiments. C.K.K. designed the virtual reality infrastructure. S.Y.L., A.B. and C.R. designed and tested the bReaChES opsin. A.J. performed injections and fibre implant surgeries. M.L. assisted with behaviour. C.L. assisted with cranial window surgeries and statistical analysis. P.R. and K.D. wrote the paper; K.D. supervised all aspects of the work. All authors discussed findings, edited and contributed to the final version of the manuscript.

Corresponding author

Correspondence to Karl Deisseroth.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Anatomical characterization of the AC–CA projection.

a, Five days after RV-tdT injection into the hippocampus (coordinates specified), retrogradely labelled neurons were detected in the contralateral hippocampus (arrow), medial septum (bracket), and AC (Fig. 1a). Scale bars: ×5: 300 μm; ×10: 100 μm (confocal). b, Eight weeks after injection of CAV-Cre into the hippocampus and DIO-eYFP in the AC (coordinates specified), afferent cell bodies were detected in the AC (arrow). Confocal image; ×10 magnification; scale bar: 100 μm. c, Retrograde tracing with RV-tdT from the AC to map reciprocal connections from the hippocampus. The injection site is indicated in brackets, sparse labelling of afferent cell bodies in left and right hippocampus, primarily in the subiculum (arrows), and also in the medial dorsal thalamic nucleus as expected (asterisk). Confocal ×5, scale bar, 200 μm; ×10 images, scale bar 100 μm.

Extended Data Figure 2 Optogenetic manipulation of the AC–CA projection.

a, Experimental design: RV-ChR2-eYFP (or eYFP alone) was injected into the dorsal hippocampus and light was delivered above the cell bodies in the AC. Five days after injection, ChR2 and eYFP mice were fear conditioned in context A while no-shock controls were only exposed to context A (day 1). All mice were tested with light on and off sessions in context B (day 2), and then tested for contextual memory retrieval in context A (day 3). Optogenetic stimulation was with 473 nm light in a train of 20 Hz, 15 ms pulses, 30 s duration, with 8–10 mW laser power at fibre tip. b, Freezing (no head motion observed) during day 2 is plotted in 5 s time bins over 150 s in context B (left). ChR2/shock (FC): black; ChR2/no shock (NS): red; eYFP/shock (eYFP): blue. Individuals in the FC group (each animal a different colour) are shown (middle). Summary (right): percentage time freezing (mean ± s.d.) 20 s before light on (darker shade) versus 20 s after light on (lighter shade); FC: 60.9 ± 7.4% light on versus 6.5 ± 4.4% light off, n = 8; NS: 2.7 ± 0.65% light on versus 3.4 ± 0.95% light off, n = 6; eYFP: 2.9 ± 0.75% light on versus 3.6 ± 1% light off, n = 6; P < 0.001, two-way ANOVA with repeated measures. c, Preservation of contextual fear memory (percentage time freezing) on day 3 in the original context (mean ± s.d., P < 0.001, unpaired t-test). d, Preservation of contextual memory in medial septum injected mice (Fig. 1f); percentage time freezing on day 3 in the original context (mean ± s.d., P < 0.001, comparisons shown, unpaired t-test). e, Preservation of contextual memory in hippocampus injected mice (Fig. 1h); percentage time freezing on day 3 in the original context (mean ± s.d., n = 8 mice, P < 0.001, paired t-test). f, The successful loss-of-function experiments targeting hippocampus-dependent memory formation mediated by cells giving rise to the AC–CA projection (reported in Fig. 2) were designed to allow the most robust inhibition of this circuit element. An alternative design (attempting to target the projection field despite the broad and long septotemporal extent of the hippocampal formation) was also explored as shown here but was not effective, as expected; we injected AAV5-eNpHR3.0-eYFP (or AAV5-eYFP in a parallel cohort) bilaterally into the AC, and targeted light stimulation bilaterally to axon terminals in the hippocampus. Eight weeks after injection, all mice were fear conditioned to context (day 1), and tested for context retrieval during light on/off sessions (day 2), and again for context retrieval in light-off only (day 3). Optogenetic inhibition was with constant illumination of 589 nm light, 30 s duration, with 8–10 mW laser power at fibre tip. g, We observed a trend towards reduction in freezing due to optical inhibition of the AC–CA projection during memory retrieval. Percentage time freezing in context A during day 2 before light (darker bar on left) versus after light (lighter bar at right). eNpHR3.0: 73.5 ± 8.5% light off versus 55.5 ± 11.4% light on, n = 10; eYFP: 74 ± 7.4% light off versus 74 ± 11.3% light on, n = 10; percentage time freezing in context A with light off (dark bars) during day 3 is shown after dotted line. eNpHR3.0: 67.5 ± 7.2%, n = 10; eYFP: 66.5 ± 9.1%, n = 10 (P = 0.067, two-way ANOVA). As expected, point illumination may be less effective for inhibiting broad axon terminal field volumes. h, Extension of findings from effective loss-of-function experiments (Fig. 2) targeting hippocampus-dependent memory formation mediated by cells giving rise to the AC–CA projection: significant effect on speed of onset of memory expression. Experimental design: CAV-Cre was injected into the dorsal hippocampus, DIO-eNpHR3.0 (or DIO-eYFP) was injected into the AC, and light was delivered above cell bodies in the AC. All mice were fear conditioned in context A (day 1), tested for latency to contextual retrieval with light-on only (day 2), and then for latency to context retrieval in light-off only (day 3). i, Day 2: 66.1 ± 18.1 s for eNpHR3.0 (n = 12) versus 43.8 ± 11.1 s for eYFP (n = 8) during light on; day 3: 53.8 ± 13.7 s for eNpHR3.0 versus 48.8 ± 7.7 s for eYFP during light off; P < 0.05 two-way ANOVA with repeated measures. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Figure 3 Physiological properties of GCaMP6m-expressing CA3 neurons.

a, To ensure that expression of GCaMP6m did not alter Ca2+-related physiological processes, we tracked a form of endocannabinoid-mediated short-term plasticity known as depolarization-induced suppression of inhibition (DSI). Schematic diagram of DSI is shown; DSI is dependent on the increase of postsynaptic intracellular Ca2+ to trigger the synthesis and release of endocannabinoids, which then signal in a retrograde fashion to suppress GABA release from presynaptic inhibitory neurons expressing cannabinoid receptors (adapted with permission from ref. 65). Intrinsic membrane properties of the GCaMP6m-expressing CA3 cells were similar to previously reported values for CA3 (ref. 66); mean resting potential: −72.1 ± 1.6 mV; mean input resistance: 161.8 ± 26.4 ΜΩ; n = 7. b, Sample trace illustrating DSI of sIPSCs in a GCaMP6m-expressing CA3 cell following application of a depolarizing current step (from −65 mV to 0 mV for 500 ms). c, Sample trace illustrating lack of DSI of sIPSCs with inclusion of the intracellular calcium chelator BAPTA (1,2-bis(o-aminophenoxy)ethane-N,N,N′,N′-tetraacetic acid) in the patch pipette. d, Summary graph of normalized charge transfer in GCaMP6m-expressing cells with standard intracellular solution (left, normalized charge transfer of sIPSCs following DSI compared with pre-pulse baseline over the same fixed interval: charge reduced to 46.9 ± 6.7% of baseline charge; n = 7; comparable to charge transfer reported for non-GCaMP-expressing cells67) and with addition of intracellular BAPTA (right, n = 6; error bars represent standard error of the mean (s.e.m.); P < 0.05, paired t-test). e, Spontaneous event rate (detection described in Methods) of GCaMP6m-expressing neurons as a function of baseline GCaMP6m fluorescence intensity (arbitrary units spanning the range over which event-rate population data could be reliably quantified) in each cell (pooling all neurons with ≥1 significant transient, from all mice, over all fields of view). Event rates were not observed to change significantly as a function of GCaMP6m expression level (Spearman’s rank correlation coefficient: 0.48, P = 0.3). f, Behavioural scores from mice before GCaMP6m virus injection and implantation of cannulae above hippocampus; lick rates for the first 2 min in the fear (black) versus neutral (grey) contexts are provided. The level of learning assessed by lick suppression on day 3 retrieval (mean 0.5 ± 0.3 for day 3 fear versus 2.7 ± 0.3 for day 3 neutral; n = 10, P < 0.001, paired t-test) pre-injection/implantation was comparable to levels corresponding to post-injection/implantation (compare with Fig. 3b). *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Figure 4 Real-time imaging of neural ensembles in three-dimensional hippocampal volumes: extraction of neural sources and identification of significant transients.

a, Head-fixed virtual reality setup. Mice run on an axially fixed track ball31 while movements and licking behaviour are measured through an optical mouse and a lickometer, respectively, both interfaced with the virtual-reality gaming software. For contextual fear conditioning, water-restricted mice were exposed to two contexts with distinct visual, olfactory, tactile and auditory cues (day 1), and provided with aversive air puffs in one context (fear context), but not the other (neutral context) (day 2). Fear memory retrieval in the two contexts was quantified (days 3, 30) by lick suppression. b, Sample mean intensity z projections from raw videos (scale bar: 50 μm), with extracted neural sources (segmented cells) from CA3 for each of the optical sections, along with the first 50 time-series traces. Scale: 300% ΔF/F, 30 s. c, Identification of significant transients in ΔF/F traces. Histogram showing the distribution of events occurring at amplitude 2σ above noise (noise calculated on a per-cell basis), over a range of event duration in seconds. The number of negative-going transients at each amplitude and duration are plotted in red to the left of the ordinate, and positive-going transients at each amplitude and duration are plotted in blue to the right. d, e, The above analysis is repeated for events that occur at an amplitude of 3σ (d) and 4σ (e). f, False positive rates for 2-, 3-, and 4-σ events (pooled across all neurons in all mice over all FOVs). False positive rate curves were calculated for each σ level by dividing the number of negative events at that level by the number of positive events at that level (Methods). Event onset was defined as the time corresponding to ΔF/F exceeding 2σ, and offset as the time corresponding to ΔF/F falling below 0.5σ. A decaying exponential was fit by least-squares to the false positive rate values, allowing for the determination of a minimum transient duration at each σ level for different confidence levels.

Extended Data Figure 5 Cell populations and graph properties of fear and neutral networks in hippocampus during retrieval.

a, No context-dependent change in total event amplitude or rate was detected. Top, mean GCaMP6m-detected event amplitude (average ΔF/F of all significant events; definition of significant event for each neuron as described in Methods) is plotted across days for mice in the fear and neutral contexts (n = 5 mice, not significant in paired t-tests). Bottom, mean GCaMP6m-detected event rate plotted across days for mice in the fear and neutral contexts (n = 5 mice, not significant in paired t-tests). b, Context-dependent changes in individual-neuron and correlated behaviour were observed. Top, number of active neurons (at least one significant GCaMP6m transient detected within first 2 min in context) plotted for fear and neutral contexts (n = 5 mice, 378 ± 64 for day 3 fear context versus 257 ± 39 for day 3 neutral context; P < 0.05, paired t-test, mean ± s.d.). Bottom, mean number of correlated pairs per neuron (where pairwise Pearson’s correlation coefficient >0.3) plotted for fear and neutral contexts (n = 5 mice, 18.5 ± 1.8 for day 2 fear context versus 13.4 ± 1.4 for day 2 neutral context; 11.3 ± 0.8 for day 3 fear context versus 13.6 ± 0.5 for day 3 neutral context; P < 0.05, paired t-test). c, Fitting histograms from Fig. 3d to an exponential distribution of the form aebx demonstrates a power-law (b>1) distribution in day 3 fear context (each red dot represents one mouse) compared to day 3 neutral context (b < 1; green dots), which was consistent across all mice (n = 5 mice; P < 0.01, paired t-test). Many graph properties were calculated for fear versus neutral context, but the power-law exponent of the degree distribution distinguished fear (red) from neutral (green) most powerfully (discriminants shown: coefficient of the power law exponent = 0.78, coefficient of cluster coefficient = 0.61, coefficient of mean path length = 0.11, with 90% confidence intervals being [0.74, 1.0], [0.1, 0.65] and [0.01, 0.23], respectively). These confidence intervals were obtained using 1,000 bootstrapped samples; shown is the best three-dimensional hyperplane separation using a linear support vector machine classifier. d, Histograms of the number of correlated partner neurons existing for each neuron in fear versus neutral context on day 3 (retrieval testing) across mice. The dotted red line indicates correlation threshold (set automatically as mean + 1 standard deviation in the number of correlated pairs in the network), to the right of which lie (by definition) the highly correlated or HC neurons. Similar measurements of interest in fear versus neutral context across mice were calculated and are provided here for other graph invariant properties: e, betweenness centrality; f, g, clique properties; h, strength; i, cluster coefficient; and j, mean path length (all defined in Methods). For the above calculations, correlation between two neurons was defined to exist when the pairwise Pearson correlation coefficient exceeded 0.3 (Methods). Data are presented as individual data points corresponding to each mouse, with mean ± s.d. *P < 0.05, **P < 0.01, ***P < 0.001, using paired t-tests.

Extended Data Figure 6 Functional relationships of fear-context-defined HC neurons as appearing in fear versus neutral context.

ac, Data from all additional mice (beyond the exemplar of Fig. 3e, f) demonstrating that HC neurons (red circles) in the fear context with a high degree of correlated partners (grey edges) when located in the neutral context have a much lower degree of correlated partners (n = 4 mice including the example in Fig. 3e, f); mean = 60 correlated pairs (standard deviation 19.4) in fear context versus mean = 18 correlated pairs (standard deviation 14.2) in neutral context; P < 0.01 by paired t-test). Only four mice are analysed here because the exact same FOV (with cell identities) was not captured in fear versus neutral context for one mouse.

Extended Data Figure 7 Spatial and temporal organization of HC neurons.

a, Plot of mean pairwise correlation versus mean pairwise distance averaged over all FOVs (all days and all contexts) from all five mice. It was possible to detect a significant but weak relationship between mean correlation and distance (Spearman’s correlation = −0.66, P = 0.01), which could be a reflection of fine-scale spatial clustering as might be expected of recurrent circuits in CA3, but would also probably include residual crosstalk between regions of interest (ROIs) due to brain motion and common neuropil signal, which is expected and not significantly different from what has been previously observed in the hippocampus3. b, Plot of the number of correlated pairs versus pairwise distance for all neurons (black line), and HC neurons only (grey line). More correlated pairs were found at greater distances for HC neurons (Spearman’s = 0.84, P = 0.002 for HC neurons; Spearman’s correlation = 0.23, P = 0.43 for all neurons). c, Cumulative distributions showing fraction of HC neurons (y-axis) with onset times at various latencies across the time course of synchronous events (x-axis) averaged across all mice, compared to response latencies of non-HC neurons. HC neuron activity appeared significantly earlier than for non-HC neurons during synchronous events (P < 0.001, Kolmogorov–Smirnov two-tail test, κ = 0.664; note the horizontal resolution of the plot is inversely proportional to length of the synchrony window, and dependent on frame duration; for instance, a 10-s-long synchrony window with frame duration of 333 ms corresponds to a 3.33% resolution per frame).

Extended Data Figure 8 Additional analyses: estimation of event onsets using fast non-negative deconvolution, and correlated pair analysis.

a, Example pairings of the original GCaMP6m trace (top traces), with the deconvolved trace (bottom traces), shows reliable estimation of event onset from deconvolved data (deconvolution algorithm and parameters detailed further in Methods). Scale bar: 150% ΔF/F, 10 s. b, Original GCaMP6m traces from a representative synchronous event in one animal (left), paired with the deconvolved traces for that same synchronous event (right). Scale bar: 300% ΔF/F, 10 s. c, The highest-degree node (neuron with the greatest number of correlated pairs) in the day 3 fear context had significantly more correlated pairs than the highest degree node in the day 3 neutral context, significant across n = 5 mice (58.8 versus 33.2 pairs, P < 0.01, paired t-test). d, Temporal relationship of HC neuron activity onset (set to time 0) compared with onset activity of correlated pairs (binned into 333 ms preceding or succeeding HC activity); n = 48 HC neurons. HCs were more likely to lead than lag their correlated pairs (58.5 ± 20% leading versus 24.4 ± 10% lagging; P < 0.01, unpaired t-test). e, Significant synchronous activity (defined in Methods) quantified across five mice: number of synchronous events in the fear context was significantly greater than in the neutral context (5.8 ± 2.9 events in fear context versus 1.2 ± 1.1 in neutral context; P < 0.01, paired t-test). *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Figure 9 PCA of population trajectories in fear versus neutral contexts.

a, PCA of ΔF/F traces of all active cells for mouse 2, performed separately for fear and neutral contexts. Population trajectories in the fear context take large, nearly orthogonal, deviations from the centre, while neutral context trajectories remain close to the origin. b, Three-dimensional reconstruction of the neuronal population showing that neurons participating in each synchronous event (red cells (t = 39), green circles (t = 92), blue circles (t = 200)) are largely non-overlapping and anatomically homogenously distributed throughout the volume. There are, however, a small fraction of neurons that participate in all three events (black circles). c, d, Data are shown for another representative mouse. Similar results were seen in all other mice. e, The ΔF/F traces for a randomly selected set of 30 neurons participating in each of three events are shown, with the greatest amount of overlap seen between t = 65 and t = 210. Scale bars: 400% ΔF/F, 20 s.

Extended Data Figure 10 bReaChES: engineering a red-shifted opsin for robust projection targeting.

a, Schematics of ReaChR31 and bReaChES. ReaChR is a hybrid of segments from channelrhopsin-1 (blue, amino acids 1–95), Volvox channelrhodopsin-1 (red, amino acids 96–246, 279–350) and Volvox channelrhodopsin-2 (green, amino acids 247–278). The VChR1 segment contains the point mutation Leu171Ile. ReaChR was modified here for enhanced expression and membrane trafficking as well as accelerated channel kinetics, resulting in bReaChES, as follows. The first 51 amino-terminal residues were replaced by the first 11 amino-terminal residues from channelrhodopsin-2 (yellow, amino acids 1–11) and the last 5 carboxy-terminal residues were removed. Mutation of Glu 123 to Ser increases speed of channel closure. b, Spectra of C1V1TT, bReaChES and ChR2 measured between 400 and 650 nm at 0.65 mW mm−2 in cultured neurons from rat hippocampus (n = 6 each). c, Stationary photocurrents at 575 nm (C1V1TT 630 ± 109 pA (s.e.m. throughout figure), ReaChR 963 ± 113 pA, bReaChES 1,365 ± 128 pA) and 632 nm (C1V1TT 315 ± 111 pA, ReaChR 1,003 ± 95 pA, bReaChES 841 ± 102 pA). Current amplitudes were measured at −80 mV and 5 mW mm−2 light intensity, respectively. d, Speed of channel closure: τ value of mono-exponential off-kinetics (C1V1TT 79 ± 3.7 ms, n = 26; ReaChR 682 ± 86 ms, n = 6; bReaChES 49 ± 4.4 ms, n = 25; P < 0.0005). e, f, Representative current-clamp traces of ReaChR- or bReaChES-expressing cultured neurons excited with 633 nm light (5 ms, 5 mW mm−2). ReaChR kinetics were slow enough that reliable action potential generation was only possible at very low frequencies (e), while the accelerated channel closure of bReaChES allowed reliable spike generation up to 20 Hz (f). g, h, Representative voltage-clamp (g) and current-clamp (h) traces of postsynaptic cells responding to light stimulation (orange) of bReaChES-expressing presynaptic terminals. Pulse length: 5 ms. i, j, Stationary photocurrents (i) and light-evoked spike probability (j) in opsin-expressing medial PFC (mPFC) cells in acute slice (C1V1TT: n = 11, bReaChES: n = 10). k, l, Light-evoked EPSC amplitude (k) and spike probability (l) in postsynaptic cells (C1V1TT: n = 10, bReaChES: n = 18). Light wavelength 575 nm (25 nm bandwidth) and power density 5 mW mm−2. *P < 0.05, **P < 0.01, ***P < 0.001.

Supplementary information

Representative video of mouse expressing AAVdj-CaMKIIα::ChR2 in anterior cingulate with optical fiber implant targeting hippocampus.

This animal had been fear conditioned in a different context, and is shown in neutral context. Video displays mouse freezing during light-on (visible at fiberoptic interface with head). Playback speed: 2x. (MOV 3815 kb)

Same mouse from video 1, but now after extinction training in original context.

Mouse displays normal movement (no freezing) during light-on. Playback speed: 2x. (MOV 4592 kb)

Same mouse from video 1, but now after re-training with fear conditioning in the original context.

Mouse again freezes during light-on sessions. Playback speed: 2x. (MOV 5813 kb)

Representative video of mouse expressing EYFP in cell bodies of AC (retrogradely labeled from hippocampus via CAV-Cre) with optical fiber implant targeting these cell bodies.

The animal has been fear conditioned the previous day and is now in the conditioned context for retrieval. Video displays mouse without reduction in freezing during light-on. Playback speed: 2x. (MOV 4048 kb)

Representative video of mouse expressing eNpHR3.0 in cell bodies of AC (retrogradely labeled from hippocampus via CAV-Cre) with optical fiber implant targeting these cell bodies.

The animal has been fear conditioned the previous day and is now in the conditioned context for retrieval. Video displays mouse with reduction in freezing during light-on. Playback speed: 2x. (MOV 4562 kb)

Representative in vivo imaging with GCAMP6m in hippocampus CA3 (one optical slice out of the volume).

Note CA3 dendrites evident in same plane (top left in FOV). Objective: 20x, 0.5NA. Resolution: 512x512. Volume refresh rate: 6 Hz using piezo motion coupled to resonant scanner imaging; image taken of plane in 33 ms (30 Hz resonant scanner). Playback speed: 5x. (AVI 6114 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rajasethupathy, P., Sankaran, S., Marshel, J. et al. Projections from neocortex mediate top-down control of memory retrieval. Nature 526, 653–659 (2015). https://doi.org/10.1038/nature15389

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature15389

Further reading

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.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing