Searching for collective behavior in a network of real neurons

TL;DR

This study constructs maximum entropy models to analyze correlated neural activity in the salamander retina, revealing complex collective behaviors, information encoding capacity, and high predictability of individual neuron states.

Contribution

It introduces K-pairwise maximum entropy models that accurately describe neural population activity, extending previous pairwise models to include global interactions.

Findings

Models accurately reproduce neural activity statistics

Neural populations encode significant visual information

Individual neuron states are highly predictable from the population

Abstract

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.