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BMC Neuroscience

Open Access

Bayesian binning for maximising information rate of rapid serial presentation for sensory neurons

BMC Neuroscience20078(Suppl 2):P151

https://doi.org/10.1186/1471-2202-8-S2-P151

Published: 6 July 2007

Understanding the response properties of single neurons is seriously limited by the available experimental time and the rate [bit/s] at which information can be gained from the neurons. A substantial improvement in the latter can be achieved by speeding up the presentation of stimuli.

We show how the novel technique of Bayesian Binning [1] can be used to find the optimal stimulus presentation rate of a continuous sequence of stimuli.

This method applied to neurons in high-level visual cortical area STSa gives optimal presentation rates of approximately 56 ms/stimulus (18 stimuli/s) which is significantly faster than conventional presentation rates, allowing a better sampling of stimulus space. We relate these results to findings obtained with the Bayesian Bin Classification method [2, 3], which can be used to select the optimal time window for the analysis of the continuous response stream. Both methods will soon be freely available as standalone command-line applications or Matlab/Octave plugins.

The optimal window duration is equal to the stimulus duration near the best presentation rate. Interestingly, this duration also corresponds to the peak of spike efficiency [bit/spike] of a rate code whose firing rates match those found in visual neurons (area STSa).

Authors’ Affiliations

(1)
School of Psychology, University of St. Andrews

References

  1. Endres D, Földiák P: Bayesian bin distribution inference and mutual information. IEEE Trans Inform Theory. 2005, 51 (11): 3766-3779. 10.1109/TIT.2005.856954.View ArticleGoogle Scholar
  2. Endres D: Bayesian and information-theoretic tools for neuroscience. PhD thesis. 2007, University of St. Andrews, digital research repository, [http://hdl.handle.net/10023/162]Google Scholar
  3. Endres D, Földiák P: Exact Bayesian bin classification: a fast alternative to Bayesian classification and its application to neural response analysis. 2007.Google Scholar

Copyright

© Endres and Földiák; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.

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