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How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network

A preprint version of the article is available at bioRxiv.

Abstract

Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)–dentate gyrus (DG)–CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC–DG–CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV+-INs) in the DG, is an efficient pattern separator. Both external gamma-modulated inhibition and internal lateral inhibition mediated by PV+-INs substantially contributed to pattern separation. Both local connectivity and fast signaling at GC–PV+-IN synapses were important for maximum effectiveness. Similarly, mossy fiber synapses with conditional detonator properties contributed to pattern separation. By contrast, perforant path synapses with Hebbian synaptic plasticity and direct EC–CA3 connection shifted the network towards pattern completion. Our results demonstrate that the specific properties of cells and synapses optimize higher-order computations in biological networks and might be useful to improve the deep learning capabilities of technical networks.

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Fig. 1: Pattern separation in a biologically realistic full-scale network model.
Fig. 2: Dependence of pattern separation on gamma rhythm and lateral inhibition.
Fig. 3: Moderate dependence of pattern separation on divergent excitatory connectivity between EC neurons and GCs.
Fig. 4: Requirement for local PN–IN interconnectivity and fast IN signaling.
Fig. 5: Contribution of hippocampal mossy fiber synapses to pattern separation.
Fig. 6: Contribution of PP input to pattern computations.

Data availability

Output datasets can be regenerated from the code76. As the full output dataset generated in this work is huge (>10 Tb), deposit in a publicly available repository is not practical at the current time point. Specific data will be provided by the corresponding author on request . Source data are provided with this paper.

Code availability

A minimal version of the Neuron simulation code is provided as Supplementary Software 1. A full version of the simulation and analysis code has been deposited in a publicly available doi-minting repository under the GNU General Public License v.3, as published by the Free Software Foundation76.

References

  1. Yassa, M. A. & Stark, C. E. Pattern separation in the hippocampus. Trends Neurosci. 34, 515–525 (2011).

    Google Scholar 

  2. Rolls, E. T. Pattern separation, completion, and categorisation in the hippocampus and neocortex. Neurobiol. Learn. Mem. 129, 4–28 (2016).

    Google Scholar 

  3. Chavlis, S. & Poirazi, P. Pattern separation in the hippocampus through the eyes of computational modeling. Synapse 71, e21972 (2017).

    Google Scholar 

  4. Cayco-Gajic, N. A. & Silver, R. A. Re-evaluating circuit mechanisms underlying pattern separation. Neuron 101, 584–602 (2019).

    Google Scholar 

  5. 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).

    Google Scholar 

  6. Scharfman, H. E. The dentate gyrus: A comprehensive guide to structure, function, and clinical implications. Progress Brain Res. 163, 627–637 (2007).

    Google Scholar 

  7. Bischofberger, J., Engel, D., Frotscher, M. & Jonas, P. Timing and efficacy of transmitter release at mossy fiber synapses in the hippocampal network. Pflügers Arch. 453, 361–372 (2006).

    Google Scholar 

  8. Guzman, S. J., Schlögl, A., Frotscher, M. & Jonas, P. Synaptic mechanisms of pattern completion in the hippocampal CA3 network. Science 353, 1117–1123 (2016).

    Google Scholar 

  9. Marr, D. A theory of cerebellar cortex. J. Physiol. 202, 437–470 (1969).

    Google Scholar 

  10. Albus, J. S. A theory of cerebellar function. Math. Biosci. 10, 25–61 (1971).

    Google Scholar 

  11. Amaral, D. G., Ishizuka, N. & Claiborne, B. Neurons, numbers and the hippocampal network. Prog. Brain Res. 83, 1–11 (1990).

    Google Scholar 

  12. Boss, B. D., Turlejski, K., Stanfield, B. B. & Cowan, W. M. On the numbers of neurons in fields CA1 and CA3 of the hippocampus of Sprague-Dawley and Wistar rats. Brain Res. 406, 280–287 (1987).

    Google Scholar 

  13. Amrein, I., Slomianka, L. & Lipp, H. P. Granule cell number, cell death and cell proliferation in the dentate gyrus of wild-living rodents. European J. Neurosci. 20, 3342–3350 (2004).

    Google Scholar 

  14. Coultrip, R., Granger, R. & Lynch, G. A cortical model of winner-take-all competition via lateral inhibition. Neural Netw. 5, 47–54 (1992).

    Google Scholar 

  15. Wiechert, M. T., Judkewitz, B., Riecke, H. & Friedrich, R. W. Mechanisms of pattern decorrelation by recurrent neuronal circuits. Nat. Neurosci. 13, 1003–1010 (2010).

    Google Scholar 

  16. Papadopoulou, M., Cassenaer, S., Nowotny, T. & Laurent, G. Normalization for sparse encoding of odors by a wide-field interneuron. Science 332, 721–725 (2011).

    Google Scholar 

  17. Lin, A. C., Bygrave, A. M., de Calignon, A., Lee, T. & Miesenböck, G. Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination. Nat. Neurosci. 17, 559–568 (2014).

    Google Scholar 

  18. Maass, W. On the computational power of winner-take-all. Neural Comput. 12, 2519–2535 (2000).

    Google Scholar 

  19. de Almeida, L., Idiart, M. & Lisman, J. E. A second function of gamma frequency oscillations: an E%-max winner-take-all mechanism selects which cells fire. J. Neurosci. 29, 7497–7503 (2009).

    Google Scholar 

  20. Tetzlaff, T., Helias, M., Einevoll, G. T. & Diesmann, M. Decorrelation of neural-network activity by inhibitory feedback. PLoS Comput. Biol. 8, e1002596 (2012).

    MathSciNet  Google Scholar 

  21. Geiger, J. R. P., Lübke, J., Roth, A., Frotscher, M. & Jonas, P. Submillisecond AMPA receptor-mediated signaling at a principal neuron-interneuron synapse. Neuron 18, 1009–1023 (1997).

    Google Scholar 

  22. Espinoza, C., Guzman, S. J., Zhang, X. & Jonas, P. Parvalbumin+ interneurons obey unique connectivity rules and establish a powerful lateral-inhibition microcircuit in dentate gyrus. Nat. Commun. 9, 4605 (2018).

    Google Scholar 

  23. O'Reilly, R. C. & McClelland, J. L. Hippocampal conjunctive encoding, storage, and recall: avoiding a trade-off. Hippocampus 4, 661–682 (1994).

    Google Scholar 

  24. Neunuebel, J. P. & Knierim, J. J. CA3 retrieves coherent representations from degraded input: direct evidence for CA3 pattern completion and dentate gyrus pattern separation. Neuron 81, 416–427 (2014).

    Google Scholar 

  25. Vyleta, N. P., Borges-Merjane, C. & Jonas, P. Plasticity-dependent, full detonation at hippocampal mossy fiber–CA3 pyramidal neuron synapses. eLife 5, e17977 (2016).

    Google Scholar 

  26. Cayco-Gajic, N. A., Clopath, C. & Silver, R. A. Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks. Nat. Commun. 8, 1116 (2017).

    Google Scholar 

  27. Witter, M. P. The perforant path: projections from the entorhinal cortex to the dentate gyrus. Prog. Brain Res. 163, 43–61 (2007).

    Google Scholar 

  28. Bliss, T. V. P. & Lømo, T. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232, 331–356 (1973).

    Google Scholar 

  29. McNaughton, B. L., Douglas, R. M. & Goddard, G. V. Synaptic enhancement in fascia dentata: cooperativity among coactive afferents. Brain Res. 157, 277–293 (1978).

    Google Scholar 

  30. McHugh, T. J. et al. Dentate gyrus NMDA receptors mediate rapid pattern separation in the hippocampal network. Science 317, 94–99 (2007).

    Google Scholar 

  31. McNaughton, B. L. & Morris, R. G. M. Hippocampal synaptic enhancement and information storage within a distributed memory system. Trends Neurosci. 10, 408–415 (1987).

    Google Scholar 

  32. Steward, O. Topographic organization of the projections from the entorhinal area to the hippocampal formation of the rat. J. Comp. Neurol. 167, 285–314 (1976).

    Google Scholar 

  33. Zhang, X., Schlögl, A. & Jonas, P. Selective routing of spatial information flow from input to output in hippocampal granule cells. Neuron 107, 1212–1225 (2020).

    Google Scholar 

  34. Valiant, L. G. The hippocampus as a stable memory allocator for cortex. Neural Comput. 24, 2873–2899 (2012).

    MathSciNet  MATH  Google Scholar 

  35. Dasgupta, S., Stevens, C. F. & Navlakha, S. A neural algorithm for a fundamental computing problem. Science 358, 793–796 (2017).

    MathSciNet  MATH  Google Scholar 

  36. Sharma J. & Navlakha, S. Improving similarity search with high-dimensional locality-sensitive hashing. Preprint at https://arxiv.org/abs/1812.01844 (2018).

  37. Bartos, M. et al. Fast synaptic inhibition promotes synchronized gamma oscillations in hippocampal interneuron networks. Proc. Natl Acad. Sci. USA 99, 13222–13227 (2002).

    Google Scholar 

  38. Claiborne, B. J., Amaral, D. G. & Cowan, W. M. A light and electron microscopic analysis of the mossy fibers of the rat dentate gyrus. J. Comp. Neurol. 246, 435–458 (1986).

    Google Scholar 

  39. Henze, D. A., Wittner, L. & Buzsáki, G. Single granule cells reliably discharge targets in the hippocampal CA3 network in vivo. Nat. Neurosci. 5, 790–795 (2002).

    Google Scholar 

  40. Vandael, D., Borges-Merjane, C., Zhang, X. & Jonas, P. Short-term plasticity at hippocampal mossy fiber synapses is induced by natural activity patterns and associated with vesicle pool engram formation. Neuron 107, 509–521 (2020).

    Google Scholar 

  41. Bragin, A. et al. Gamma (40–100 Hz) oscillation in the hippocampus of the behaving rat. J. Neurosci. 15, 47–60 (1995).

    Google Scholar 

  42. Pernía-Andrade, A. J. & Jonas, P. Theta-gamma-modulated synaptic currents in hippocampal granule cells in vivo define a mechanism for network oscillations. Neuron 81, 140–152 (2014).

    Google Scholar 

  43. Majani, E., Erlanson, R. & Abu-Mostafa, Y. On the k-winners takes-all network. Adv. Neural Inf. Process. Syst. 1, 634–642 (1989).

    Google Scholar 

  44. Ellias, S. A. & Grossberg, S. Pattern formation, contrast control, and oscillations in the short term memory of shunting on-center off-surround networks. Biol. Cybern. 20, 69–98 (1975).

    MathSciNet  MATH  Google Scholar 

  45. Tamamaki, N. & Nojyo, Y. Projection of the entorhinal layer II neurons in the rat as revealed by intracellular pressure-injection of neurobiotin. Hippocampus 3, 471–480 (1993).

    Google Scholar 

  46. Hu, H., Gan, J. & Jonas, P. Fast-spiking, parvalbumin+ GABAergic interneurons: from cellular design to microcircuit function. Science 345, 1255263 (2014).

    Google Scholar 

  47. Nörenberg, A., Hu, H., Vida, I., Bartos, M. & Jonas, P. Distinct nonuniform cable properties optimize rapid and efficient activation of fast-spiking GABAergic interneurons. Proc. Natl Acad. Sci. USA 107, 894–899 (2010).

    Google Scholar 

  48. Kraushaar, U. & Jonas, P. Efficacy and stability of quantal GABA release at a hippocampal interneuron-principal neuron synapse. J. Neurosci. 20, 5594–5607 (2000).

    Google Scholar 

  49. Chamberland, S., Timofeeva, Y., Evstratova, A., Volynski, K. & Tóth, K. Action potential counting at giant mossy fiber terminals gates information transfer in the hippocampus. Proc. Natl Acad. Sci. USA 115, 7434–7439 (2018).

    Google Scholar 

  50. Toth, K., Suares, G., Lawrence, J. J., Philips-Tansey, E. & McBain, C. J. Differential mechanisms of transmission at three types of mossy fiber synapse. J. Neurosci. 20, 8279–8289 (2000).

    Google Scholar 

  51. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Google Scholar 

  52. Babadi, B. & Sompolinsky, H. Sparseness and expansion in sensory representations. Neuron 83, 1213–1226 (2014).

    Google Scholar 

  53. de la Rocha, J., Doiron, B., Shea-Brown, E., Josić, K. & Reyes, A. Correlation between neural spike trains increases with firing rate. Nature 448, 802–806 (2007).

    Google Scholar 

  54. Hoeffding, W. Masstabinvariante Korrelationsstheorie. Schriften Math. Instituts Angew. Math. Univ. Berlin 5, 179–233 (1940).

    Google Scholar 

  55. Kowalski, J., Gan, J., Jonas, P. & Pernía-Andrade, A. J. Intrinsic membrane properties determine hippocampal differential firing pattern in vivo in anesthetized rats. Hippocampus 26, 668–682 (2016).

    Google Scholar 

  56. Engin, E. et al. Tonic inhibitory control of dentate gyrus granule cells by α5-containing GABAA receptors reduces memory interference. J. Neurosci. 35, 13698–13712 (2015).

    Google Scholar 

  57. Espinoza Martinez, C. M. Parvalbumin+ Interneurons Enable Efficient Pattern Separation in Hippocampal Microcircuits (IST Austria, 2019); https://doi.org/10.15479/AT:ISTA:6363

  58. Braganza, O., Mueller-Komorowska, D., Kelly, T. & Beck, H. Quantitative properties of a feedback circuit predict frequency-dependent pattern separation. eLife 9, e53148 (2020).

    Google Scholar 

  59. Bartos, M., Vida, I., Frotscher, M., Geiger, J. R. P. & Jonas, P. Rapid signaling at inhibitory synapses in a dentate gyrus interneuron network. J. Neurosci. 21, 2687–2698 (2001).

    Google Scholar 

  60. Hu, H. & Jonas, P. A supercritical density of Na+ channels ensures fast signaling in GABAergic interneuron axons. Nat. Neurosci. 17, 686–693 (2014).

    Google Scholar 

  61. Bucurenciu, I., Kulik, A., Schwaller, B., Frotscher, M. & Jonas, P. Nanodomain coupling between Ca2+ channels and Ca2+ sensors promotes fast and efficient transmitter release at a cortical GABAergic synapse. Neuron 57, 536–545 (2008).

    Google Scholar 

  62. Jones, B. W. et al. Targeted deletion of AKAP7 in dentate granule cells impairs spatial discrimination. eLife 5, e20695 (2016).

    Google Scholar 

  63. Pehlevan, C., Sengupta, A. M. & Chklovskii, D. B. Why do similarity matching objectives lead to Hebbian/Anti-Hebbian networks? Neural Comput. 30, 84–124 (2018).

    MathSciNet  MATH  Google Scholar 

  64. Myers, C. E. & Scharfman, H. E. A role for hilar cells in pattern separation in the dentate gyrus: a computational approach. Hippocampus 19, 321–337 (2009).

    Google Scholar 

  65. Johnston, S. T., Shtrahman, M., Parylak, S., Gonçalves, J. T. & Gage, F. H. Paradox of pattern separation and adult neurogenesis: A dual role for new neurons balancing memory resolution and robustness. Neurobiol. Learn. Mem. 129, 60–68 (2016).

    Google Scholar 

  66. Schneider, C. J., Bezaire, M. & Soltesz, I. Toward a full-scale computational model of the rat dentate gyrus. Front. Neural Circuits 6, 83 (2012).

    Google Scholar 

  67. Wang, X. J. & Buzsáki, G. Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J. Neurosci.16, 6402–6413 (1996).

    Google Scholar 

  68. Ermentrout, B. Type I membranes, phase resetting curves, and synchrony. Neural Comput. 8, 979–1001 (1996).

    Google Scholar 

  69. Carnevale, N. T. & Hines, M. L. The Neuron Book (Cambridge Univ. Press, 2006).

  70. Schmidt-Hieber, C., Jonas, P. & Bischofberger, J. Subthreshold dendritic signal processing and coincidence detection in dentate gyrus granule cells. J. Neurosci. 27, 8430–8441 (2007).

    Google Scholar 

  71. Paxinos, G. & Franklin, K. The Mouse Brain in Stereotaxic Coordinates 4th edn (Academic, 2012).

  72. Han, Z. S., Buhl, E. H., Lörinczi, Z. & Somogyi, P. A high degree of spatial selectivity in the axonal and dendritic domains of physiologically identified local-circuit neurons in the dentate gyrus of the rat hippocampus. European J. Neurosci. 5, 395–410 (1993).

    Google Scholar 

  73. Hefft, S. & Jonas, P. Asynchronous GABA release generates long-lasting inhibition at a hippocampal interneuron-principal neuron synapse. Nat. Neurosci. 8, 1319–1328 (2005).

    Google Scholar 

  74. Hosp, J. A. et al. Morpho-physiological criteria divide dentate gyrus interneurons into classes. Hippocampus 24, 189–203 (2014).

    Google Scholar 

  75. Armstrong, C. & Soltesz, I. Basket cell dichotomy in microcircuit function. J. Physiol. 590, 683–694 (2012).

    Google Scholar 

  76. Guzman, S. J. et al. Pattern Separation Network (IST Austria, 2021); https://doi.org/10.15479/AT:ISTA:10110

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Acknowledgements

We thank A. Aertsen, N. Kopell, W. Maass, A. Roth, F. Stella and T. Vogels for critically reading earlier versions of the manuscript. We are grateful to F. Marr and C. Altmutter for excellent technical assistance, E. Kralli-Beller for manuscript editing, and the Scientific Service Units of IST Austria for efficient support. Finally, we thank T. Carnevale, L. Erdös, M. Hines, D. Nykamp and D. Schröder for useful discussions, and R. Friedrich and S. Wiechert for sharing unpublished data. This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 692692, P.J.) and the Fond zur Förderung der Wissenschaftlichen Forschung (Z 312-B27, Wittgenstein award to P.J. and P 31815 to S.J.G.).

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Contributions

P.J. and S.J.G. designed the model and the layout of the simulations. P.J. and A.S. performed large-scale simulations on computer clusters. C.E., X.Z. and B.A.S. provided experimental data. P.J. and S.J.G. analyzed the data. P.J. wrote the paper and all authors jointly revised it.

Corresponding author

Correspondence to Peter Jonas.

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The authors declare no competing interests.

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Peer review information Nature Computational Science thanks Ad Aertsen, Alessandro Treves and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Extended data

Extended Data Fig. 1 Quantitative analysis of pattern separation in neuronal networks.

a, b, Schematic illustration of pattern separation. (a) Neuronal activity at the input (top) and the output level (bottom) during two similar contexts (top). Red, cells active in pattern A; green, cells active in pattern B; yellow, cells active in both patterns. (b) Overlay of neuronal activity at the input (top) and the output level (bottom). Highly overlapping input patterns (A, B; top) are converted into weakly overlapping output patterns (A′, B′; bottom). Modified from Johnston et al., 2016 (ref. 65). c, d, Analysis of pattern separation and pattern completion in input-output correlation plots (RoutRin graphs). Rin and Rout represent pairwise correlations in input and output patterns. Red dashed line indicates pattern identity. Area below identity line (red and green stripes, c) represents a regime in which Rout < Rin, that is, pattern separation. Area above identity line (yellow area, d) corresponds to a regime where Rout > Rin, that is, pattern completion. Insets, Venn diagrams of two patterns before and after pattern separation (c) and pattern completion (d). e, f, Quantitative analysis of RoutRin graphs. Data points (black points) represent output and input correlations for all pairs of patterns; 4950 data points total. An integral-based metric, ψ, provides a robust assessment of the average pattern separation behavior (e, main panel). ψ was computed as the area between identity line (IL, red dashed line) and the interpolated RoutRin curve (light gray area), normalized to the maximum area (0.5). A slope-based measure, γ, provides a selective analysis of pattern separation in a region of interest in which differences between input patterns are small (e, inset). γ was computed as the slope of the RoutRin curve for Rin → 1. A rank correlation-based measure, ρ, provides an analysis of the ability of the network to preserve rank order similarity (f). ρ was computed as the Pearson’s correlation coefficient of the ranks of all Rout versus the ranks of all Rin data points. RoutRin plot and rank correlation plots are shown for standard model parameters (same data as in Fig. 1c, f; see Supplementary Table 1).

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and Table 1.

Reporting Summary

Supplementary Software 1

Zipped files for example simulations.

Source data

Source Data Fig. 1

Original values Rout versus Rin plot to obtain Psi and Gamma, rank correlation plot to obtain Rho.

Source Data Fig. 2

Original values. Fig. 2b: Activity, Psi, Gamma, and Rho as a function of Iμ. Figs. 2c,d: Psi as a function of Iμ and Jgamma with LI and without LI. Fig. 2e–g: Psi for different cEI, cIE, sigmaEI, sigmaIE, JEI and JIE.

Source Data Fig. 3

Original values. Fig. 3c: Psi as a function of nEC and nGC. Fig. 3d: Psi as a function of nEC:nGC ratio. Fig. 3f,g: Psi for different nEC, alphaEC, cEC-GC and sigmaEC-GC.

Source Data Fig. 4

Original values. Fig. 4b: Psi for different sigmaEI and sigmaIE. Fig. 4c: Psi as a function of sigmaEI and sigmaIE. Fig. 4d: Distribution of delay E-I and delay I-E. Fig. 4e,f: Psi for different deltasynE, deltasynI, tauE and taum.

Source Data Fig. 5

Original values. Fig. 5d: Psi as a function of number of MFBs. Fig. 5f: Psi as a function of MFB synaptic strength.

Source Data Fig. 6

Original values. Fig. 6c: Psi as a function of LTP at EC–GC synapses. Fig. 6f: Psi as a function of Imu EC–CA3.

Source Data Extended Data Fig. 1

Original values for Rout versus Rin plot to obtain Psi and Gamma, rank correlation plot to obtain Rho.

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Guzman, S.J., Schlögl, A., Espinoza, C. et al. How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network. Nat Comput Sci 1, 830–842 (2021). https://doi.org/10.1038/s43588-021-00157-1

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