A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas.

Maximilian Schmidt,Rembrandt Bakker,Sacha Jennifer Van Albada,Gleb Bezgin,Kelly Shen,Markus Diesmann,Lyle J Graham

doi:10.1371/journal.pcbi.1006359

Abstract

Cortical activity has distinct features across scales, from the spiking statistics of individual cells to global resting-state networks. We here describe the first full-density multi-area spiking network model of cortex, using macaque visual cortex as a test system. The model represents each area by a microcircuit with area-specific architecture and features layer- and population-resolved connectivity between areas. Simulations reveal a structured asynchronous irregular ground state. In a metastable regime, the network reproduces spiking statistics from electrophysiological recordings and cortico-cortical interaction patterns in fMRI functional connectivity under resting-state conditions. Stable inter-area propagation is supported by cortico-cortical synapses that are moderately strong onto excitatory neurons and stronger onto inhibitory neurons. Causal interactions depend on both cortical structure and the dynamical state of populations. Activity propagates mainly in the feedback direction, similar to experimental results associated with visual imagery and sleep. The model unifies local and large-scale accounts of cortex, and clarifies how the detailed connectivity of cortex shapes its dynamics on multiple scales. Based on our simulations, we hypothesize that in the spontaneous condition the brain operates in a metastable regime where cortico-cortical projections target excitatory and inhibitory populations in a balanced manner that produces substantial inter-area interactions while maintaining global stability.

Highlights

Cortical activity has distinct but interdependent features on local and global scales, molded by multi-scale connectivity
The mammalian cortex fulfills its complex tasks by operating on multiple temporal and spatial scales from single cells to entire areas comprising millions of cells. These multiscale dynamics are supported by specific network structures at all levels of organization
We present a full-density multi-scale spiking network model in which these features emerge from its detailed structure

Summary

Introduction

Cortical activity has distinct but interdependent features on local and global scales, molded by multi-scale connectivity. The second describes large-scale cortical dynamics by simplifying ensemble dynamics to few differential equations These models predict resting-state oscillations in a metastable regime [16,17,18,19] and reproduce the frequency specificity of inter-area interactions [20]. LIF neurons independent Poisson spikes (for each neuron, fixed rate νext = νbgkext with average external spike rate νbg = 10 spikes/s and number of external inputs per population kext, weight J) We adapt their population-specific connectivity matrix to the compositions of the 32 areas, thereby keeping the proportion of synapses that a neuron in a given population receives from a given other population constant. The detailed derivation is published in [35]

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Conclusion