Neural Comput - Neural information processing with feedback modulations.

Tópicos

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Resumo

Descending feedback connections, together with ascending feedforward ones, are the indispensable parts of the sensory pathways in the central nervous system. This study investigates the potential roles of feedback interactions in neural information processing. We consider a two-layer continuous attractor neural network (CANN), in which neurons in the first layer receive feedback inputs from those in the second one. By utilizing the intrinsic property of a CANN, we use a projection method to reduce the dimensionality of the network dynamics significantly. The simplified dynamics allows us to elucidate the effects of feedback modulation analytically. We find that positive feedback enhances the stability of the network state, leading to an improved population decoding performance, whereas negative feedback increases the mobility of the network state, inducing spontaneously moving bumps. For strong, negative feedback interaction, the network response to a moving stimulus can lead the actual stimulus position, achieving an anticipative behavior. The biological implications of these findings are discussed. The simulation results agree well with our theoretical analysis.

Resumo Limpo

descend feedback connect togeth ascend feedforward one indispens part sensori pathway central nervous system studi investig potenti role feedback interact neural inform process consid twolay continu attractor neural network cann neuron first layer receiv feedback input second one util intrins properti cann use project method reduc dimension network dynam signific simplifi dynam allow us elucid effect feedback modul analyt find posit feedback enhanc stabil network state lead improv popul decod perform wherea negat feedback increas mobil network state induc spontan move bump strong negat feedback interact network respons move stimulus can lead actual stimulus posit achiev anticip behavior biolog implic find discuss simul result agre well theoret analysi

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