Neural Comput - Spike-based probabilistic inference in analog graphical models using interspike-interval coding.

Tópicos

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Resumo

Temporal spike codes play a crucial role in neural information processing. In particular, there is strong experimental evidence that interspike intervals (ISIs) are used for stimulus representation in neural systems. However, very few algorithmic principles exploit the benefits of such temporal codes for probabilistic inference of stimuli or decisions. Here, we describe and rigorously prove the functional properties of a spike-based processor that uses ISI distributions to perform probabilistic inference. The abstract processor architecture serves as a building block for more concrete, neural implementations of the belief-propagation (BP) algorithm in arbitrary graphical models (e.g., Bayesian networks and factor graphs). The distributed nature of graphical models matches well with the architectural and functional constraints imposed by biology. In our model, ISI distributions represent the BP messages exchanged between factor nodes, leading to the interpretation of a single spike as a random sample that follows such a distribution. We verify the abstract processor model by numerical simulation in full graphs, and demonstrate that it can be applied even in the presence of analog variables. As a particular example, we also show results of a concrete, neural implementation of the processor, although in principle our approach is more flexible and allows different neurobiological interpretations. Furthermore, electrophysiological data from area LIP during behavioral experiments are assessed in light of ISI coding, leading to concrete testable, quantitative predictions and a more accurate description of these data compared to hitherto existing models.

Resumo Limpo

tempor spike code play crucial role neural inform process particular strong experiment evid interspik interv isi use stimulus represent neural system howev algorithm principl exploit benefit tempor code probabilist infer stimuli decis describ rigor prove function properti spikebas processor use isi distribut perform probabilist infer abstract processor architectur serv build block concret neural implement beliefpropag bp algorithm arbitrari graphic model eg bayesian network factor graph distribut natur graphic model match well architectur function constraint impos biolog model isi distribut repres bp messag exchang factor node lead interpret singl spike random sampl follow distribut verifi abstract processor model numer simul full graph demonstr can appli even presenc analog variabl particular exampl also show result concret neural implement processor although principl approach flexibl allow differ neurobiolog interpret furthermor electrophysiolog data area lip behavior experi assess light isi code lead concret testabl quantit predict accur descript data compar hitherto exist model

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