Fundamental activity constraints lead to specific interpretations of the connectome

TL;DR

This paper introduces a method that uses activity constraints and a mean-field approach to refine brain network models, ensuring realistic activity patterns while revealing critical network components and guiding future experiments.

Contribution

It presents a novel approach combining activity constraints with mean-field reduction to systematically refine connectome models for realistic neural activity.

Findings

Realistic activity patterns achieved with minimal structural adjustments

Network operates near an instability point

Method identifies critical network components for dynamics

Abstract

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function.