Neural Comput - Abstract stimulus-specific adaptation models.

Tópicos

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Resumo

Many neurons that initially respond to a stimulus stop responding if the stimulus is presented repeatedly but recover their response if a different stimulus is presented. This phenomenon is referred to as stimulus-specific adaptation (SSA). SSA has been investigated extensively using oddball experiments, which measure the responses of a neuron to sequences of stimuli. Neurons that exhibit SSA respond less vigorously to common stimuli, and the metric typically used to quantify this difference is the SSA index (SI). This article presents the first detailed analysis of the SI metric by examining the question: How should a system (e.g., a neuron) respond to stochastic input if it is to maximize the SI of its output? Questions like this one are particularly relevant to those wishing to construct computational models of SSA. If an artificial neural network receives stimulus information at a particular rate and must respond within a fixed time, what is the highest SI one can reasonably expect? We demonstrate that the optimum, average SI is constrained by the information in the input source, the length and encoding of the memory, and the assumptions concerning how the task is decomposed.

Resumo Limpo

mani neuron initi respond stimulus stop respond stimulus present repeat recov respons differ stimulus present phenomenon refer stimulusspecif adapt ssa ssa investig extens use oddbal experi measur respons neuron sequenc stimuli neuron exhibit ssa respond less vigor common stimuli metric typic use quantifi differ ssa index si articl present first detail analysi si metric examin question system eg neuron respond stochast input maxim si output question like one particular relev wish construct comput model ssa artifici neural network receiv stimulus inform particular rate must respond within fix time highest si one can reason expect demonstr optimum averag si constrain inform input sourc length encod memori assumpt concern task decompos

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