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Biol Cybern (2006) 94: 180191 DOI 10.1007/s00422-005-0039-3
 

Summary: Biol Cybern (2006) 94: 180191
DOI 10.1007/s00422-005-0039-3
ORIGINAL PAPER
Benoit Gaillard Hilary Buxton Jianfeng Feng
Population approach to a neural discrimination task
Received: 24 May 2004 / Accepted: 1 November 2005 / Published online: 6 December 2005
Springer-Verlag 2005
Abstract This article gives insights into the possible neuro-
nal 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 second-
order 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 discrim-

  

Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra
Feng, Jianfeng - Centre for Scientific Computing and Computer Science, University of Warwick

 

Collections: Computer Technologies and Information Sciences