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.