Population approach to a neural discrimination task
Gaillard, Benoit, Buxton, H. and Feng, Jianfeng. (2006) Population approach to a neural discrimination task. Biological Cybernetics, Vol.94 (No.3). pp. 180-191. ISSN 0340-1200Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/s00422-005-0039-3
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 neural model that performs the discrimination 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 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 discrimination accuracy but only at the expense of the speed at which we can read out the FR.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
|Divisions:||Faculty of Science > Computer Science
Faculty of Science > Centre for Scientific Computing
|Journal or Publication Title:||Biological Cybernetics|
|Official Date:||March 2006|
|Number of Pages:||12|
|Page Range:||pp. 180-191|
|Access rights to Published version:||Restricted or Subscription Access|
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