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

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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.

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Acknowledgements

We thank Phil Bream, Hélène Seiler and Yang Zhang for their contributions to earlier work leading up to that reported here, and Aman Saleem for useful discussions and comments on this manuscript. We also thank Yasser Roudi for useful comments on the TAP equations, and Jascha Sohl-Dickstein, Peter Battaglino, and Michael R DeWeese for useful MATLAB code and discussion of the MPFL technique. This work was supported by EPSRC grant EP/E002331/1 to SRS.

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Schaub, M.T., Schultz, S.R. The Ising decoder: reading out the activity of large neural ensembles. J Comput Neurosci 32, 101–118 (2012). https://doi.org/10.1007/s10827-011-0342-z

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