Neural Comput - Dynamics of feature categorization.

Tópicos

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Resumo

In visual and auditory scenes, we are able to identify shared features among sensory objects and group them according to their similarity. This grouping is preattentive and fast and is thought of as an elementary form of categorization by which objects sharing similar features are clustered in some abstract perceptual space. It is unclear what neuronal mechanisms underlie this fast categorization. Here we propose a neuromechanistic model of fast feature categorization based on the framework of continuous attractor networks. The mechanism for category formation does not rely on learning and is based on biologically plausible assumptions, for example, the existence of populations of neurons tuned to feature values, feature-specific interactions, and subthreshold-evoked responses upon the presentation of single objects. When the network is presented with a sequence of stimuli characterized by some feature, the network sums the evoked responses and provides a running estimate of the distribution of features in the input stream. If the distribution of features is structured into different components or peaks (i.e., is multimodal), recurrent excitation amplifies the response of activated neurons, and categories are singled out as emerging localized patterns of elevated neuronal activity (bumps), centered at the centroid of each cluster. The emergence of bump states through sequential, subthreshold activation and the dependence on input statistics is a novel application of attractor networks. We show that the extraction and representation of multiple categories are facilitated by the rich attractor structure of the network, which can sustain multiple stable activity patterns for a robust range of connectivity parameters compatible with cortical physiology.

Resumo Limpo

visual auditori scene abl identifi share featur among sensori object group accord similar group preattent fast thought elementari form categor object share similar featur cluster abstract perceptu space unclear neuron mechan underli fast categor propos neuromechanist model fast featur categor base framework continu attractor network mechan categori format reli learn base biolog plausibl assumpt exampl exist popul neuron tune featur valu featurespecif interact subthresholdevok respons upon present singl object network present sequenc stimuli character featur network sum evok respons provid run estim distribut featur input stream distribut featur structur differ compon peak ie multimod recurr excit amplifi respons activ neuron categori singl emerg local pattern elev neuron activ bump center centroid cluster emerg bump state sequenti subthreshold activ depend input statist novel applic attractor network show extract represent multipl categori facilit rich attractor structur network can sustain multipl stabl activ pattern robust rang connect paramet compat cortic physiolog

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