The Ising Model for Neural Data: Model Quality and Approximate Methods for Extracting Functional Connectivity

TL;DR

This paper evaluates pairwise Ising models for multi-neuron spike train data, comparing exact and approximate methods for coupling estimation, and assesses their effectiveness in capturing neural network statistics.

Contribution

It introduces efficient approximate methods for estimating couplings in large neural networks and analyzes their accuracy and limitations compared to exact Boltzmann learning.

Findings

Approximate methods like TAP and Sessak-Monasson are highly accurate.

Estimating couplings from smaller subsets overestimates their true values.

Model fit quality decreases with larger neuron subsets, indicating the need for higher-order correlations.

Abstract

We study pairwise Ising models for describing the statistics of multi-neuron spike trains, using data from a simulated cortical network. We explore efficient ways of finding the optimal couplings in these models and examine their statistical properties. To do this, we extract the optimal couplings for subsets of size up to 200 neurons, essentially exactly, using Boltzmann learning. We then study the quality of several approximate methods for finding the couplings by comparing their results with those found from Boltzmann learning. Two of these methods- inversion of the TAP equations and an approximation proposed by Sessak and Monasson- are remarkably accurate. Using these approximations for larger subsets of neurons, we find that extracting couplings using data from a subset smaller than the full network tends systematically to overestimate their magnitude. This effect is described qualitatively by infinite-range spin glass theory for the normal phase. We also show that a globally-correlated input to the neurons in the network lead to a small increase in the average coupling. However, the pair-to-pair variation of the couplings is much larger than this and reflects intrinsic properties of the network. Finally, we study the quality of these models by comparing their entropies with that of the data. We find that they perform well for small subsets of the neurons in the network, but the fit quality starts to deteriorate as the subset size grows, signalling the need to include higher order correlations to describe the statistics of large networks.