A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields
2007 (English)In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 22, no 2, 135-146 p.Article in journal (Refereed) Published
Computational models of primary visual cortexhave demonstrated that principles of efficient coding andneuronal sparseness can explain the emergence of neuroneswith localised oriented receptive fields. Yet, existing modelshave failed to predict the diverse shapes of receptive fieldsthat occur in nature. The existing models used a particular“soft” form of sparseness that limits average neuronal activity.Here we study models of efficient coding in a broadercontext by comparing soft and “hard” forms of neuronalsparseness.As a result of our analyses, we propose a novel networkmodel for visual cortex. Themodel forms efficient visual representationsin which the number of active neurones, ratherthan mean neuronal activity, is limited. This form of hardsparseness also economises cortical resources like synapticmemory and metabolic energy. Furthermore, our model accuratelypredicts the distribution of receptive field shapesfound in the primary visual cortex of cat and monkey.
Place, publisher, year, edition, pages
2007. Vol. 22, no 2, 135-146 p.
Biological vision, Sparse coding, Receptive field learning
IdentifiersURN: urn:nbn:se:kth:diva-6304DOI: 10.1007/s10827-006-0003-9ISI: 000244296700003PubMedID: 17053994ScopusID: 2-s2.0-33847100046OAI: oai:DiVA.org:kth-6304DiVA: diva2:10983
QC 201009162006-11-012006-11-012011-11-07Bibliographically approved