A comprehensive data-driven model of cat primary visual cortex

Abstract

Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue: a comprehensive spiking model of cat primary visual cortex satisfying an unprecedented range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward drive elicited by a high diversity of visual contexts, the simulated network produces a realistic, quantitatively accurate interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code.This integrative model offers numerous insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception. Significance statementComputational modeling can integrate fragments of understanding generated by experimental neuroscience. However, most previous models considered only a few features of neural computation at a time, leading to either poorly constrained models with many parameters, or lack of expressiveness in over-simplified models. A solution is to commit to detailed models, but constrain them with a broad range of anatomical and functional data. This requires a long-term systematic approach. Here we present a first snapshot of such an integrative program: a large-scale spiking model of V1, that is constrained by an unprecedented range of anatomical and functional features. Together with the associated modeling infrastructure, this study lays the groundwork for a broad integrative modeling program seeking an in-depth understanding of vision. Keywordsvisual cortex, cortical microcircuit, large-scale models, comprehensive modeling, layered network 2 resting-conductance regime emerges in the model. Under visual stimulation, the same model exhibits diversity of the interplay between evoked excitation and inhibition; stimulus-locked subthreshold variability; contrast-invariant orientation tuning; size tuning; stimulus-dependent firing precision; and a realistic distribution of Simple/Complex receptive fields.This study advances our ability to capture the function of V1 within a single model, and provides insights into how the studied properties interac...

Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

“…The variants were constrained by a plethora of experimental data: the representation of the individual cells and their firing behavior in response to somatic current injections, LGN filters, thalamocortical connectivity, recurrent connectivity, and activity patterns observed in vivo. This work continues the trend of developing increasingly more sophisticated models of cortical circuits in general (e.g., Traub et al, 2005;Zhu, Shelley and Shapley, 2009;Potjans and Diesmann, 2014;Markram et al, 2015;Joglekar et al, 2018;Schmidt et al, 2018) and visual cortex in particular (Wehmeier et al, 1989;Troyer et al, 1998;Zemel and Sejnowski, 1998;Krukowski and Miller, 2001;Arkhipov et al, 2018;Antolík et al, 2019). Our main goal was to integrate existing and, especially, emerging multi-modal experimental datasets describing the structure and in vivo activity of cortical circuits into biologically realistic network models.…”

“…Simulating cortical circuits has a long history (e.g. (Wehmeier et al, 1989;Zemel and Sejnowski, 1998;Troyer et al, 1998;Krukowski and Miller, 2001;Traub et al, 2005;Zhu, Shelley and Shapley, 2009;Potjans and Diesmann, 2014;Markram et al, 2015;Arkhipov et al, 2018;Joglekar et al, 2018;Schmidt et al, 2018;Antolík et al, 2019;Schwalger and Chizhov, 2019)), with models incrementally building upon their predecessors. The simulations described here are a further instance of this evolution toward digital simulacra that predict new experiments, are insightful, and ever more faithful to the vast complexity of cortical tissue, in particular its heterogeneous neuronal cell classes, connections, and in vivo activity.…”

Cortical visual prosthesis: a detailed large-scale simulation study

“…This manuscript relies on five key simulation components: (1) a stimulation strategy that translates a class of visual stimuli into driving signals for a MLEE, (2) a model of the MLEE, (3) a model of light propagation through cortical tissue, (4) a model of light illumination dependent channelrhodopsin (ChR) dynamics in transfected cells, and (5) a detailed large-scale model of primary visual cortex (whose parametrization and justification were developed elsewhere [10]). As illustrated in figure 1, the chaining of these five components allows us to simulate the effects of light stimulation in a ChR transfected primary visual cortex given a specific stimulation strategy.…”

“…This work has been based on our recent detailed large-scale model of primary visual cortex [10]. The model has been implemented using the Mozaik neural simulation workflow framework [8] and the Arkheia tool [7].…”

“…In this study we address both these issues using a detailed large-scale spiking neuron based modeling strategy of high biological fidelity. Specifically, using a combination of a model of light propagation in cortical tissue , model of channelrhodopsin dynamics, and a previous model of anatomically and functionally calibrated V1 cortical circuitry [10], we examine the cortical dynamics in a virtual population of transfected V1 neurons as a function of light stimulation parameters (figure 1). We demonstrate that performing these tests in a detailed recurrent model of cortex is crucial, as the simulated neural responses follow radically different patterns when the cortical circuitry is disabled.…”