Tree-structured neural decoding

  • Authors:

  • Christian d'Avignon;Donald Geman

  • Affiliations:

  • Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 720 Rutland Avenue, Traylor Building 608, Baltimore, MD;Department of Mathematical Sciences and Whitaker Biomedical Engineering Institute, Johns Hopkins University, 3400 N. Charles Street, Clark Hall 302A, Baltimore, MD

  • Venue:
  • The Journal of Machine Learning Research
  • Year:
  • 2003

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Abstract

We propose adaptive testing as a general mechanism for extracting information about stimuli from spike trains. Each test or question corresponds to choosing a neuron and a time interval and checking for a given number of spikes. No assumptions are made about the distribution of spikes or any other aspect of neural encoding. The chosen questions are those which most reduce the uncertainty about the stimulus, as measured by entropy and estimated from stimulus-response data. Our experiments are based on accurate simulations of responses to pure tones in the auditory nerve and are meant to illustrate the ideas rather than investigate the auditory system. The results cohere nicely with well-understood encoding of amplitude and frequency in the auditory nerve, suggesting that adaptive testing might provide a powerful tool for investigating complex and poorly understood neural structures.