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

article How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network published yes José Guzmán author 30CC5506-F248-11E8-B48F-1D18A9856A870000-0003-2209-5242 Alois Schlögl author 45BF87EE-F248-11E8-B48F-1D18A9856A870000-0002-5621-8100 Claudia Espinoza Martinez author 31FFEE2E-F248-11E8-B48F-1D18A9856A870000-0003-4710-2082 Xiaomin Zhang author 423EC9C2-F248-11E8-B48F-1D18A9856A870000-0003-0256-6529 Benjamin Suter author 4952F31E-F248-11E8-B48F-1D18A9856A870000-0002-9885-6936 Peter M Jonas author 353C1B58-F248-11E8-B48F-1D18A9856A870000-0001-5001-4804 PeJo department Biophysics and circuit function of a giant cortical glutamatergic synapse project Synaptic communication in neuronal microcircuits project 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. https://research-explorer.ista.ac.at/download/10816/11430/Guzmanetal2021.pdf application/pdf2022-06-17no https://research-explorer.ista.ac.at/download/10816/11431/Guzmanetal2021Suppl.pdf application/pdf2022-06-17no Springer Nature2021 eng Nature Computational Science 2662-8457 00088856750001510.1038/s43588-021-00157-1 112830-842 https://research-explorer.ista.ac.at/record/10110 https://ista.ac.at/en/news/spot-the-difference/ J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, P.M. Jonas, Nature Computational Science 1 (2021) 830–842. Guzmán J, Schlögl A, Espinoza Martinez C, Zhang X, Suter B, Jonas PM. 2021. How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network. Nature Computational Science. 1(12), 830–842. Guzmán, José, Alois Schlögl, Claudia Espinoza Martinez, Xiaomin Zhang, Benjamin Suter, and Peter M Jonas. “How Connectivity Rules and Synaptic Properties Shape the Efficacy of Pattern Separation in the Entorhinal Cortex–Dentate Gyrus–CA3 Network.” Nature Computational Science. Springer Nature, 2021. <a href="https://doi.org/10.1038/s43588-021-00157-1">https://doi.org/10.1038/s43588-021-00157-1</a>. Guzmán J, Schlögl A, Espinoza Martinez C, Zhang X, Suter B, Jonas PM. How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network. Nature Computational Science. 2021;1(12):830-842. doi:<a href="https://doi.org/10.1038/s43588-021-00157-1">10.1038/s43588-021-00157-1</a> J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, and P. M. Jonas, “How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network,” Nature Computational Science, vol. 1, no. 12. Springer Nature, pp. 830–842, 2021. Guzmán, José, et al. “How Connectivity Rules and Synaptic Properties Shape the Efficacy of Pattern Separation in the Entorhinal Cortex–Dentate Gyrus–CA3 Network.” Nature Computational Science, vol. 1, no. 12, Springer Nature, 2021, pp. 830–42, doi:<a href="https://doi.org/10.1038/s43588-021-00157-1">10.1038/s43588-021-00157-1</a>. Guzmán, J., Schlögl, A., Espinoza Martinez, C., Zhang, X., Suter, B., & Jonas, P. M. (2021). How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network. Nature Computational Science. Springer Nature. <a href="https://doi.org/10.1038/s43588-021-00157-1">https://doi.org/10.1038/s43588-021-00157-1</a> 108162022-03-04T08:32:36Z2026-07-28T12:15:17Z