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The use of decoding to analyze the contribution to the information of the correlations between the firing of simultaneously recorded neurons

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Abstract

A new decoding method is described that enables the information that is encoded by simultaneously recorded neurons to be measured. The algorithm measures the information that is contained not only in the number of spikes from each neuron, but also in the cross-correlations between the neuronal firing including stimulus-dependent synchronization effects. The approach enables the effects of interactions between the ‘signal’ and ‘noise’ correlations to be identified and measured, as well as those from stimulus-dependent cross-correlations. The approach provides an estimate of the statistical significance of the stimulus-dependent synchronization information, as well as enabling its magnitude to be compared with the magnitude of the spike-count related information, and also whether these two contributions are additive or redundant. The algorithm operates even with limited numbers of trials. The algorithm is validated by simulation. It was then used to analyze neuronal data from the primate inferior temporal visual cortex. The main conclusions from experiments with two to four simultaneously recorded neurons were that almost all of the information was available in the spike counts of the neurons; that this Rate information included on average very little redundancy arising from stimulus-independent correlation effects; and that stimulus-dependent cross-correlation effects (i.e. stimulus-dependent synchronization) contribute very little to the encoding of information in the inferior temporal visual cortex about which object or face has been presented.

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Notes

  1. When using the Gaussian, the probabilities of 0, 1, 2, 3, etc., spikes were estimated as follows for each stimulus. The probability of zero spikes was obtained directly by the proportion of trials that had 0 spikes. The mean and standard deviation of the positive part of the Gaussian were computed from the remaining spike counts. We note that because the spike counts are approximately Poisson distributed, the variance increases in proportion to (and equals) the mean. A consequence of this is that the mean is located one standard deviation above zero (assuming that the Poisson is a good fit). Thus any inaccuracies due to truncating the fitted Gaussian below 0 are small, because only a small fraction of the data lie more than one standard deviation below the mean. In practice, this truncated Gaussian was chosen over the Poisson distribution (with an additional weight at r c =0), because we have found previously (Rolls et al. 1997) and with the present data set that with our neuronal populations the Gaussian fit produces slightly higher values for both percentage correct and information. However, the fact that the performance with the Poisson fit was almost as good as the truncated Gaussian fit indicates that truncation per se is probably not a major issue. The indication that the Poisson fit does not work quite as well as the Gaussian fit with our data reflects the fact that the variability of the spike counts does not fit a Poisson distribution perfectly, and the spike count distributions can be fitted better using the two parameters provided by the Gaussian fitting procedure. The theoretical advantages of using different types of decoding for the spike counts are considered in the “Discussion.”

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Acknowledgements

This research was supported by the Medical Research Council, grant PG9826105, by the Human Frontier Science Program, by the MRC Interdisciplinary Research Centre for Cognitive Neuroscience, and by the Wellcome Trust.

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Authors and Affiliations

  1. Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK

    Leonardo Franco, Edmund T. Rolls & Nikolaos C. Aggelopoulos

  2. SISSA-Cognitive Neuroscience Sector, Via Beirut 2–4, 34014, Trieste, Italy

    Alessandro Treves

Authors
  1. Leonardo Franco
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  2. Edmund T. Rolls
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  3. Nikolaos C. Aggelopoulos
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  4. Alessandro Treves
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Correspondence to Edmund T. Rolls.

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Franco, L., Rolls, E.T., Aggelopoulos, N.C. et al. The use of decoding to analyze the contribution to the information of the correlations between the firing of simultaneously recorded neurons. Exp Brain Res 155, 370–384 (2004). https://doi.org/10.1007/s00221-003-1737-5

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