Inference in neural networks using conditional mean-field methods

TL;DR

This paper introduces a mean-field approach for neural network inference that accurately estimates correlations and reconstructs network dynamics, outperforming previous methods especially near phase transitions.

Contribution

The authors extend mean-field methods to include correlation estimation in non-equilibrium neural models, providing a fast inference tool validated on synthetic and real neural data.

Findings

Outperforms previous methods in correlation estimation

Successfully reconstructs network dynamics from data near phase transitions

Effective on both synthetic and in vitro neural recordings

Abstract

We extend previous mean-field approaches for non-equilibrium neural network models to estimate correlations in the system. This offers a powerful tool for approximating the system dynamics as well as a fast method to infer network parameters from observations. We develop our method in an asymmetric kinetic Ising model and test its performance on 1) synthetic data generated by an asymmetric version of the Sherrington Kirkpatric model and 2) recordings of in vitro neuron spiking activity from the mouse somatosensory cortex. We find that our mean-field method outperforms previous ones in estimating networks correlations and successfully reconstructs network dynamics from data near a phase transition showing large fluctuations.