A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex

Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.We thank Michael Hines for helping with synapse model implementation in NEURON; Mariana Vargas-Caballero for sharing NMDAR data; Veronica Egger for sharing in vitro data and for clarifications on the analysis methods; Jesper Sjöström for sharing in vitro data, helpful discussions, and feedback on the manuscript; Ralf Schneggenburger for helpful discussions and clarifications on the NMDAR calcium current model; Fabien Delalondre for helpful discussions; Francesco Casalegno and Taylor Newton for helpful discussion on model fitting; Daniel Keller for helpful discussions on the biophysics of synaptic plasticity; Natali Barros-Zulaica for helpful discussions on MVR modeling and generalization; Srikanth Ramaswamy, Michael Reimann and Max Nolte for feedback on the manuscript; Wulfram Gerstner and Guillaume Bellec for helpful discussions on synaptic plasticity modeling. This study was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne, from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology. E.B.M. received additional support from the CHU Sainte-Justine Research Center (CHUSJRC), the Institute for Data Valorization (IVADO), Fonds de Recherche du Québec–Santé (FRQS), the Canada CIFAR AI Chairs Program, the Quebec Institute for Artificial Intelligence (Mila), and Google. R.B.P. and J.DF. received support from the Spanish “Ministerio de Ciencia e Innovación” (grant PGC2018-094307-B-I00). M.D. and I.S. were supported by a grant from the ETH domain for the Blue Brain Project, the Gatsby Charitable Foundation, and the Drahi Family Foundation

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)