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Data-driven integration of
hippocampal CA1 synaptic physiology in silico

Authors: András Ecker 1, Armando Romani 1, Sára Sáray 2,3, Szabolcs Káli 2,3, Michele Migliore 4, Audrey Mercer 5, Henry Markram 1, Eilif Muller 1, Michael W. Reimann 1, Srikanth Ramaswamy 1,

Author information: 1 Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, Geneva, Switzerland, 2 Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary, 3 Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary, 4 Institute of Biophysics, National Research Council, Palermo, Italy, 5 UCL School of Pharmacy, University College London, London, United Kingdom.

Corresponding authors: András Ecker ( ), Srikanth Ramaswamy ( )

Journal: Hippocampus

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Citation: Ecker, A., Romani, A., Sáray, S., Káli, S., Migliore, M., Falck, J., Lange, S., Mercer, A., Thomson, A. M., Muller, E., Reimann, M. W., & Ramaswamy, S. (2020). Data‐driven integration of hippocampal CA1 synaptic physiology in silico. Hippocampus, Wiley. 30(11), 1129–1145.


Licence: This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

The anatomy and physiology of synaptic connections in rodent hippocampal CA1 have been exhaustively characterized in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data-driven approach to integrate the current state-of-the-art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo-dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short-term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired-recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource towards a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.

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