Modelling a Visual Discrimination Task

We study the performance of a spiking network model based on Integrate-and-Fire neurons when performing a benchmark discrimination task. The task consists of determining the direction of moving dots in a noisy context. By varying the synaptic parameters of the Integrate-and-Fire neurons, we illustrate the counter-intuitive importance of the second order statistics (input noise) in improving the discrimination accuracy of the model. Surprisingly we found that measuring the Firing Rate (FR) of a population of neurons considerably enhances the discrimination accuracy as well, in comparison with the firing rate of a single neuron

www.elsevier.com/locate/neucom Modelling a visual discrimination task

We study the performance of a spiking network model based on integrate-and-fire neurons when performing a benchmark discrimination task. The task consists of determining the direction of moving dots in a noisy context. By varying the synaptic parameters of the integrate-and-fire neurons,we illustrate the counter-intuitive importance of the secondorder statistics (input noise) in improving the discrimination accuracy of the model. Surprisingly,we found that measuring the firing rate (FR) of a population of neurons considerably enhances the discrimination accuracy as well,in comparison with the firing rate of a single neuron. r 2004 Elsevier B.V. All rights reserved