Spin glass models for a network of real neurons

TL;DR

This paper models neural activity using Ising models, demonstrating that pairwise interactions explain higher-order correlations, and explores how large neural networks operate near critical points to optimize coding capacity.

Contribution

It introduces Ising models for neural populations, showing their effectiveness in capturing correlations and analyzing network behavior near criticality for neural coding.

Findings

Pairwise interactions explain higher-order correlations.

Networks operate closer to critical points as they grow larger.

Metastable states can serve as neural code words.

Abstract

Ising models with pairwise interactions are the least structured, or maximum-entropy, probability distributions that exactly reproduce measured pairwise correlations between spins. Here we use this equivalence to construct Ising models that describe the correlated spiking activity of populations of 40 neurons in the salamander retina responding to natural movies. We show that pairwise interactions between neurons account for observed higher-order correlations, and that for groups of 10 or more neurons pairwise interactions can no longer be regarded as small perturbations in an independent system. We then construct network ensembles that generalize the network instances observed in the experiment, and study their thermodynamic behavior and coding capacity. Based on this construction, we can also create synthetic networks of 120 neurons, and find that with increasing size the networks operate closer to a critical point and start exhibiting collective behaviors reminiscent of spin glasses. We examine closely two such behaviors that could be relevant for neural code: tuning of the network to the critical point to maximize the ability to encode diverse stimuli, and using the metastable states of the Ising Hamiltonian as neural code words.