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

Nature Computational Science volume 1, pages 830–842 (2021)Cite this article

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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.

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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.

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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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Author notes

  1. S. Jose Guzman

    Present address: Institute of Molecular Biotechnology, Vienna, Austria

  2. Claudia Espinoza

    Present address: Medical University of Austria, Division of Cognitive Neurobiology, Vienna, Austria

  3. Xiaomin Zhang

    Present address: Brain Research Institute, University of Zürich, Zurich, Switzerland

Authors and Affiliations

  1. IST Austria, Klosterneuburg, Austria

    S. Jose Guzman, Alois Schlögl, Claudia Espinoza, Xiaomin Zhang, Benjamin A. Suter & Peter Jonas

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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.

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Correspondence to Peter Jonas.

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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 (Rout–Rin 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 Rout–Rin 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 Rout–Rin 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 Rout–Rin 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. Rout–Rin 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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