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Titolo: Characterizing the sparseness of neural codes
Autore: Willmore, B; Tolhurst, DJ;
 Indirizzi:
 Univ Cambridge, Dept Physiol, Cambridge CB2 3EG, England Univ Cambridge Cambridge England CB2 3EG iol, Cambridge CB2 3EG, England
 Titolo Testata:
 NETWORKCOMPUTATION IN NEURAL SYSTEMS
fascicolo: 3,
volume: 12,
anno: 2001,
pagine: 255  270
 SICI:
 0954898X(200108)12:3<255:CTSONC>2.0.ZU;2L
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 INDEPENDENT COMPONENT ANALYSIS; PRIMARY VISUALCORTEX; RECEPTIVEFIELD PROFILES; CAT STRIATE CORTEX; NATURAL IMAGES; SPATIAL STRUCTURE; CORTICALCELLS; NEURONS; FILTERS; RESPONSES;
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 Engineering, Computing & Technology
 Citazioni:
 39
 Recensione:
 Indirizzi per estratti:
 Indirizzo: Willmore, B Univ Cambridge, Dept Physiol, Downing St, Cambridge CB2 3EG, England Univ Cambridge Downing St Cambridge England CB2 3EG , England



 Citazione:
 B. Willmore e D.J. Tolhurst, "Characterizing the sparseness of neural codes", NETWORKCOM, 12(3), 2001, pp. 255270
Abstract
It is often suggested that efficient neural codes for natural visual information should be 'sparse'. However, the term 'sparse' has been used in two different waysfirstly to describe codes in which few neurons are active atany time ('population sparseness'), and secondly to describe codes in which each neuron's lifetime response distribution has high kurtosis ('lifetimesparseness'). Although these ideas are related, they are not identical, and the most common measure of lifetime sparsenessthe kurtosis of the lifetime response distributions of the neuronsprovides no information about population sparseness. We have measured the population sparseness and lifetime kurtosis of several biologically inspired coding schemes. We used three measures of population sparseness (population kurtosis, TrevesRolls sparseness and 'activity sparseness'), and found them to be in close agreement with one another. However, we also measured the lifetime kurtosis of the cells in each code. We found that lifetime kurtosis is uncorrelated with population sparseness for the codes we used. Lifetime kurtosis is not. therefore, a useful measure of the population sparseness of a code. Moreover, the Gaborlike codes, which are often assumedto have high population sparseness (since they have high lifetime kurtosis), actually turned out to have rather low population sparseness. Surprisingly, principal components filters produced the codes with the highest population sparseness.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 22/01/20 alle ore 18:43:03