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What can spike train distances tell us about the neural code?
- Source :
- Recercat. Dipósit de la Recerca de Catalunya, instname, Journal of neuroscience methods 199 (2011): 146–165. doi:10.1016/j.jneumeth.2011.05.002, info:cnr-pdr/source/autori:Chicharro D. (a,b); Kreuz T. (b,c); Andrzejak RG (a)/titolo:What can spike train distances tell us about the neural code?/doi:10.1016%2Fj.jneumeth.2011.05.002/rivista:Journal of neuroscience methods/anno:2011/pagina_da:146/pagina_a:165/intervallo_pagine:146–165/volume:199
- Publication Year :
- 2011
- Publisher :
- Elsevier BV, 2011.
-
Abstract
- Time scale parametric spike train distances like the Victor and the van Rossum distances/nare often applied to study the neural code based on neural stimuli discrimination./nDifferent neural coding hypotheses, such as rate or coincidence coding,/ncan be assessed by combining a time scale parametric spike train distance with a/nclassifier in order to obtain the optimal discrimination performance. The time scale/nfor which the responses to different stimuli are distinguished best is assumed to be/nthe discriminative precision of the neural code. The relevance of temporal coding/nis evaluated by comparing the optimal discrimination performance with the one/nachieved when assuming a rate code./nWe here characterize the measures quantifying the discrimination performance,/nthe discriminative precision, and the relevance of temporal coding. Furthermore,/nwe evaluate the information these quantities provide about the neural code. We/nshow that the discriminative precision is too unspecific to be interpreted in terms/nof the time scales relevant for encoding. Accordingly, the time scale parametric/nnature of the distances is mainly an advantage because it allows maximizing the/ndiscrimination performance across a whole set of measures with different sensitivities/ndetermined by the time scale parameter, but not due to the possibility to/nexamine the temporal properties of the neural code. DC is supported by the grant 2010FI-B2 00079 of the ”Comissionat per a Universitats/ni Recerca del Departament d’Innovació, Universitats i Empresa de la Generalitat/nde Catalunya i del Fons Social Europeu” and grant 2008BE1 00166 of the ”Comissionat per a Universitats i Recerca del Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya”. RGA acknowledges grant BFU2007-61710 of the Spanish Ministry of Education and Science.
- Subjects :
- Time Factors
Sensory Receptor Cells
Computer science
Speech recognition
Spike train
Models, Neurological
Neural coding
Action Potentials
Coincidence
Discriminative model
Discrimination
Reaction Time
Xarxes neuronals (Informàtica)
Humans
Spike trains
Computer Simulation
Poisson Distribution
Parametric statistics
Temporal coding
Afferent Pathways
Quantitative Biology::Neurons and Cognition
business.industry
General Neuroscience
Discriminant Analysis
Pattern recognition
Mutual information
Precision
Spike train distances
Artificial intelligence
business
Neurociència computacional
Classifier (UML)
Scale parameter
Algorithms
Subjects
Details
- ISSN :
- 01650270
- Volume :
- 199
- Database :
- OpenAIRE
- Journal :
- Journal of Neuroscience Methods
- Accession number :
- edsair.doi.dedup.....069a39b945dfa4a3ef5920268425aea3