Assessing the Relevance of Specific Response Features in the Neural Code

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http://hdl.handle.net/10138/298369

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Eyherabide , H G & Samengo , I 2018 , ' Assessing the Relevance of Specific Response Features in the Neural Code ' , Entropy , vol. 20 , no. 11 , 879 . https://doi.org/10.3390/e20110879

Title: Assessing the Relevance of Specific Response Features in the Neural Code
Author: Eyherabide, Hugo Gabriel; Samengo, Ines
Contributor: University of Helsinki, Helsinki Institute for Information Technology
Date: 2018-11
Language: eng
Number of pages: 33
Belongs to series: Entropy
ISSN: 1099-4300
URI: http://hdl.handle.net/10138/298369
Abstract: The study of the neural code aims at deciphering how the nervous system maps external stimuli into neural activitythe encoding phaseand subsequently transforms such activity into adequate responses to the original stimulithe decoding phase. Several information-theoretical methods have been proposed to assess the relevance of individual response features, as for example, the spike count of a given neuron, or the amount of correlation in the activity of two cells. These methods work under the premise that the relevance of a feature is reflected in the information loss that is induced by eliminating the feature from the response. The alternative methods differ in the procedure by which the tested feature is removed, and the algorithm with which the lost information is calculated. Here we compare these methods, and show that more often than not, each method assigns a different relevance to the tested feature. We demonstrate that the differences are both quantitative and qualitative, and connect them with the method employed to remove the tested feature, as well as the procedure to calculate the lost information. By studying a collection of carefully designed examples, and working on analytic derivations, we identify the conditions under which the relevance of features diagnosed by different methods can be ranked, or sometimes even equated. The condition for equality involves both the amount and the type of information contributed by the tested feature. We conclude that the quest for relevant response features is more delicate than previously thought, and may yield to multiple answers depending on methodological subtleties.
Subject: neural code
representation
decoding
spike-time precision
discrimination
noise correlations
information theory
mismatched decoding
VISUAL INFORMATION
POPULATION
CORTEX
INDEPENDENCE
UNCERTAINTY
CONNECTIONS
REDUNDANCY
PRECISION
SYNERGY
BRAIN
113 Computer and information sciences
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