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J Comput Neurosci (2012) 32:101118 DOI 10.1007/s10827-011-0342-z
The Ising decoder: reading out the activity of large neural ensembles
Michael T. Schaub Simon R. Schultz
Received: 9 September 2010 / Revised: 28 April 2011 / Accepted: 22 May 2011 / Published online: 11 June 2011 Springer Science+Business Media, LLC 2011
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 ThoulessAndersonPalmer (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 re-
Action Editor: Jonathan David Victor
M. T. Schaub S. R. Schultz (B)
Department of Bioengineering, Imperial College London, South Kensington, London SW7 2AZ, UKe-mail: [email protected]
sults demonstrate the practicality of using the Ising model to read out, or decode, spatial patterns of activity comprised of many hundreds of neurons.
Keywords Neural coding Decoding algorithm
Brain machine interface Brain computer interface
BCI Correlations Synchrony Visual cortex
Spatial patterns
1 Introduction
Interpreting the patterns of activity fired by populations of neurons is one of the central challenges of modern systems neuroscience. The design of decoding algorithms capable of millisecond-by-millisecond readout of sensory or behavioural correlates of neuronal activity patterns would be a valuable step...