Neural Comput - Bayesian active learning of neural firing rate maps with transformed gaussian process priors.

Tópicos

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Resumo

A firing rate map, also known as a tuning curve, describes the nonlinear relationship between a neuron's spike rate and a low-dimensional stimulus (e.g., orientation, head direction, contrast, color). Here we investigate Bayesian active learning methods for estimating firing rate maps in closed-loop neurophysiology experiments. These methods can accelerate the characterization of such maps through the intelligent, adaptive selection of stimuli. Specifically, we explore the manner in which the prior and utility function used in Bayesian active learning affect stimulus selection and performance. Our approach relies on a flexible model that involves a nonlinearly transformed gaussian process (GP) prior over maps and conditionally Poisson spiking. We show that infomax learning, which selects stimuli to maximize the information gain about the firing rate map, exhibits strong dependence on the seemingly innocuous choice of nonlinear transformation function. We derive an alternate utility function that selects stimuli to minimize the average posterior variance of the firing rate map and analyze the surprising relationship between prior parameterization, stimulus selection, and active learning performance in GP-Poisson models. We apply these methods to color tuning measurements of neurons in macaque primary visual cortex.

Resumo Limpo

fire rate map also known tune curv describ nonlinear relationship neuron spike rate lowdimension stimulus eg orient head direct contrast color investig bayesian activ learn method estim fire rate map closedloop neurophysiolog experi method can acceler character map intellig adapt select stimuli specif explor manner prior util function use bayesian activ learn affect stimulus select perform approach reli flexibl model involv nonlinear transform gaussian process gp prior map condit poisson spike show infomax learn select stimuli maxim inform gain fire rate map exhibit strong depend seem innocu choic nonlinear transform function deriv altern util function select stimuli minim averag posterior varianc fire rate map analyz surpris relationship prior parameter stimulus select activ learn perform gppoisson model appli method color tune measur neuron macaqu primari visual cortex

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