From asynchronous states to Griffiths phases and back: structural heterogeneity and homeostasis in excitatory-inhibitory networks

TL;DR

This paper explores how structural heterogeneity in neural networks leads to different dynamical regimes, such as asynchronous states and Griffiths phases, and highlights the role of synaptic plasticity and homeostasis in maintaining functional balance.

Contribution

It introduces a simple neural network model that unifies asynchronous states and Griffiths phases within a single phase diagram, emphasizing the importance of homeostatic mechanisms.

Findings

Heterogeneity induces Griffiths phases with distinct dynamical properties.

Synaptic plasticity and homeostasis can restore excitation-inhibition balance.

Diverse dynamical regimes emerge from self-organizing processes.

Abstract

Balanced neural networks -- in which excitatory and inhibitory inputs compensate each other on average -- give rise to a dynamical phase dominated by fluctuations called asynchronous state, crucial for brain functioning. However, structural disorder -- which is inherent to random networks -- can hinder such an excitation-inhibition balance. Indeed, structural and synaptic heterogeneities can generate extended regions in phase space akin to critical points, called Griffiths phases, with dynamical features very different from those of asynchronous states. Here, we study a simple neural-network model with tunable levels of heterogeneity able to display these two types of dynamical regimes -- i.e., asynchronous states and Griffiths phases -- putting them together within a single phase diagram. Using this simple model, we are able to emphasize the crucial role played by synaptic plasticity and homeostasis to re-establish balance in intrinsically heterogeneous networks. Overall, we shed light onto how diverse dynamical regimes, each with different functional advantages, can emerge from a given network as a result of self-organizing homeostatic mechanisms.