Neural Comput - Multiplicative gain modulation arises through unsupervised learning in a predictive coding model of cortical function.

Tópicos

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Resumo

The combination of two or more population-coded signals in a neural model of predictive coding can give rise to multiplicative gain modulation in the response properties of individual neurons. Synaptic weights generating these multiplicative response properties can be learned using an unsupervised, Hebbian learning rule. The behavior of the model is compared to empirical data on gaze-dependent gain modulation of cortical cells and found to be in good agreement with a range of physiological observations. Furthermore, it is demonstrated that the model can learn to represent a set of basis functions. This letter thus connects an often-observed neurophysiological phenomenon and important neurocomputational principle (gain modulation) with an influential theory of brain operation (predictive coding).

Resumo Limpo

combin two populationcod signal neural model predict code can give rise multipl gain modul respons properti individu neuron synapt weight generat multipl respons properti can learn use unsupervis hebbian learn rule behavior model compar empir data gazedepend gain modul cortic cell found good agreement rang physiolog observ furthermor demonstr model can learn repres set basi function letter thus connect oftenobserv neurophysiolog phenomenon import neurocomput principl gain modul influenti theori brain oper predict code

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