Abstract

A computational model for the representation of visualstimuli with a population of spiking neurons is presented.We show that under mild conditions it is possible to faith-fully represent an analog video stream into a sequence ofspike trains and provide an algorithm that recovers thevideo input by using only the spike times of the popula-tion.In our model an analog, bandlimited in time, videostream approaches the dendritic trees of a neural popula-tion. At each neuron, the multi-dimensional video inputis filtered by the neuron's spatiotemporal receptive field,and the one-dimensional output dendritic current entersthe soma of the neuron (see Figure 1). The set of the spa-tial receptive fields is modeled as a Gabor filterbank. The

Highlights

  • A computational model for the representation of visual stimuli with a population of spiking neurons is presented

  • We show that under mild conditions it is possible to faithfully represent an analog video stream into a sequence of spike trains and provide an algorithm that recovers the video input by using only the spike times of the population

  • The multi-dimensional video input is filtered by the neuron's spatiotemporal receptive field, and the one-dimensional output dendritic current enters the soma of the neuron

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Summary

Introduction

A computational model for the representation of visual stimuli with a population of spiking neurons is presented. We show that under mild conditions it is possible to faithfully represent an analog video stream into a sequence of spike trains and provide an algorithm that recovers the video input by using only the spike times of the population. In our model an analog, bandlimited in time, video stream approaches the dendritic trees of a neural population.

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