The Ising decoder: reading out the activity of large neural ensembles

TL;DR

This paper demonstrates how the Ising model, combined with mean field approximations and advanced learning algorithms, can effectively decode large neural ensemble activity patterns in real-time, improving neural decoding accuracy.

Contribution

It introduces practical methods for large-scale neural decoding using the Ising model with mean field approximations and efficient learning algorithms.

Findings

Mean field approaches enable rapid inference in large neural ensembles.

Decoding performance improves with heterogeneous neural response models.

The Ising decoder is practical for real-time decoding of hundreds of neurons.

Abstract

The Ising Model has recently received much attention for the statistical description of neural spike train data. In this paper, we propose and demonstrate its use for building decoders capable of predicting, on a millisecond timescale, the stimulus represented by a pattern of neural activity. After fitting to a training dataset, the Ising decoder can be applied "online" for instantaneous decoding of test data. While such models can be fit exactly using Boltzmann learning, this approach rapidly becomes computationally intractable as neural ensemble size increases. We show that several approaches, including the Thouless-Anderson-Palmer (TAP) mean field approach from statistical physics, and the recently developed Minimum Probability Flow Learning (MPFL) algorithm, can be used for rapid inference of model parameters in large-scale neural ensembles. Use of the Ising model for decoding, unlike other problems such as functional connectivity estimation, requires estimation of the partition function. As this involves summation over all possible responses, this step can be limiting. Mean field approaches avoid this problem by providing an analytical expression for the partition function. We demonstrate these decoding techniques by applying them to simulated neural ensemble responses from a mouse visual cortex model, finding an improvement in decoder performance for a model with heterogeneous as opposed to homogeneous neural tuning and response properties. Our results demonstrate the practicality of using the Ising model to read out, or decode, spatial patterns of activity comprised of many hundreds of neurons.