Spontaneous and stimulus-induced coherent states of critically balanced neuronal networks

Takashi Hayakawa,Tomoki Fukai

doi:10.1103/physrevresearch.2.013253

Abstract

How the information microscopically processed by individual neurons is integrated and used in organizing the behavior of an animal is a central question in neuroscience. The coherence of neuronal dynamics over different scales has been suggested as a clue to the mechanisms underlying this integration. Balanced excitation and inhibition may amplify microscopic fluctuations to a macroscopic level, thus providing a mechanism for generating coherent multiscale dynamics. Previous theories of brain dynamics, however, were restricted to cases in which inhibition dominated excitation and suppressed fluctuations in the macroscopic population activity. In the present study, we investigate the dynamics of neuronal networks at a critical point between excitation-dominant and inhibition-dominant states. In these networks, the microscopic fluctuations are amplified by the strong excitation and inhibition to drive the macroscopic dynamics, while the macroscopic dynamics determine the statistics of the microscopic fluctuations. Developing a novel type of mean-field theory applicable to this class of interscale interactions, we show that the amplification mechanism generates spontaneous, irregular macroscopic rhythms similar to those observed in the brain. Through the same mechanism, microscopic inputs to a small number of neurons effectively entrain the dynamics of the whole network. These network dynamics undergo a probabilistic transition to a coherent state, as the magnitude of either the balanced excitation and inhibition or the external inputs is increased. Our mean-field theory successfully predicts the behavior of this model. Furthermore, we numerically demonstrate that the coherent dynamics can be used for state-dependent read-out of information from the network. These results show a novel form of neuronal information processing that connects neuronal dynamics on different scales.

Highlights

The cerebral cortex and hippocampus, the areas believed to be the origin of the versatile intelligent functionality of the mammalian brain, exhibit characteristic activities on two different scales
We present a solution to this fundamental issue by constructing a novel type of meanfield theory (MFT) for densely connected randomly connected neuronal networks (RNNs) with Dale’s law, for which mean synaptic weights are set to critical values between those for excitationdominant and inhibition-dominant states
Unlike the previous theories of critical dynamics, we show that fluctuations in individual neuronal activities are amplified by the strong excitation and inhibition to provide stochastic driving forces for the pop√ulation dynamics

Summary

Introduction

The cerebral cortex and hippocampus, the areas believed to be the origin of the versatile intelligent functionality of the mammalian brain, exhibit characteristic activities on two different scales. Neurons in these areas display various temporal patterns of firing activities in response to external stimuli or to being driven internally. These activities are correlated with fine features of the information the animal is processing [1,2,3,4,5]. Electroencephalograms (EEGs) and measurements of local-field potentials (LFPs) have revealed a diverse range of rhythmic activities. These vary in both frequency and amplitude, but they are clearly correlated with the behavioral states of the animal, such as its attention and arousal levels [6,7,8,9]. In recent years, increasing numbers of experimental results have suggested that coherence of activities

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