Robust entropy requires strong and balanced excitatory and inhibitory synapses

TL;DR

This paper investigates how the balance of excitatory and inhibitory synapses affects neural network entropy, revealing that a small inhibitory fraction yields robust, high entropy configurations.

Contribution

It provides an analytical framework showing that optimal entropy occurs at the excitation-inhibition boundary and highlights the importance of synapse strength and inhibitory neuron fraction.

Findings

Maximum entropy at excitation-inhibition boundary

Weak synapses yield high but fragile entropy

Small inhibitory fraction enhances robustness and entropy

Abstract

It is widely appreciated that well-balanced excitation and inhibition are necessary for proper function in neural networks. However, in principle, such balance could be achieved by many possible configurations of excitatory and inhibitory strengths, and relative numbers of excitatory and inhibitory neurons. For instance, a given level of excitation could be balanced by either numerous inhibitory neurons with weak synapses, or few inhibitory neurons with strong synapses. Among the continuum of different but balanced configurations, why should any particular configuration be favored? Here we address this question in the context of the entropy of network dynamics by studying an analytically tractable network of binary neurons. We find that entropy is highest at the boundary between excitation-dominant and inhibition-dominant regimes. Entropy also varies along this boundary with a trade-off between high and robust entropy: weak synapse strengths yield high network entropy which is fragile to parameter variations, while strong synapse strengths yield a lower, but more robust, network entropy. In the case where inhibitory and excitatory synapses are constrained to have similar strength, we find that a small, but non-zero fraction of inhibitory neurons, like that seen in mammalian cortex, results in robust and relatively high entropy.