Population approach to a neural discrimination task

Abstract This article gives insights into the possible neuronal processes involved in visual discrimination. We study the performance of a spiking network of Integrate-and-Fire (IF) neurons when performing a benchmark discrimination task. The task we adopted consists of determining the direction of moving dots in a noisy context using similar stimuli to those in the experiments of Newsome and colleagues . We present a neuralmodelthatperformsthediscrimination involved in this task. By varying the synaptic parameters of the IF neurons, we illustrate the counter-intuitive importance of the secondorder statistics (input noise) in improving the discrimination accuracy of the model. We show that measuring the Firing Rate (FR) over a population enables the model to discriminate in realistic times, and even surprisingly significantly increases its discrimination accuracy over the single neuron case, despite the faster processing. We also show that increasing the input noise increases the discr...

• Type: Journal
• Year: 2006
• Where: BC
• Authors: Benoit Gaillard, Hilary Buxton, Jianfeng Feng
• Keywords: BC 2006, Benchmark Discrimination Task, Discrimination Accuracy, Possible Neuronal Processes