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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
What can spike train distances tell us about the neural code?
Daniel Chicharro1, Thomas Kreuz, Ralph G Andrzejak
1Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain. daniel.chicharro@upf.edu
Journal of Neuroscience Methods
|May 19, 2011
Summary
Time scale parametric spike train distances help assess neural codes by optimizing stimulus discrimination. However, discriminative precision is too unspecific to reveal encoding time scales, limiting temporal property analysis.
Area of Science:
- Computational neuroscience
- Neural coding analysis
Background:
- Spike train distances, like Victor and van Rossum, are used to study neural codes.
- These methods assess coding hypotheses (rate vs. coincidence) via stimulus discrimination.
Purpose of the Study:
- To characterize measures of discrimination performance, precision, and temporal coding relevance.
- To evaluate the information these quantities provide about neural codes.
Main Methods:
- Utilizing time scale parametric spike train distances with classifiers.
- Comparing optimal discrimination performance with rate coding performance.
Main Results:
- Discriminative precision is too unspecific for direct interpretation of encoding time scales.
- Parametric distances primarily enhance discrimination performance, not temporal property examination.
Conclusions:
- The utility of parametric spike train distances lies in maximizing discrimination, not in revealing specific neural encoding time scales.
- The temporal aspect of neural codes is not effectively elucidated by current discriminative precision measures.
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