Abstract
The study of the storage and transmission of information in neural networks is an important and challenging field of research. Studies in this area aim to understand the process by which the neural systems encode and process information originating from the environment. This work aims to develop a computational model that can be used to study how neural systems encode and relate information about external stimuli. To fulfill this purpose, a computational model composed by a spiking neuron network is developed to perform a task of relational categorization that consists in measuring the relation between the intensities of two signals applied to network. A Genetic Algorithm is used to optimize the synaptic weights of the network. The results show that the network is able to perform the task of relational categorization according to a threshold defined as error rate, as well as shows that the ability of the network to detect the relation between the signals depends on the minimum and maximum difference in the number of spikes in a given time window.
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The authors thank the support of CAPES-Brazil, CNPq-Brazil, FAPEMIG, and CEFET-MG.
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Alves, L.F., Araujo Junior, F.L., Santos, B.A., Gomes, R.M. (2017). A Network of Spiking Neurons Performing a Relational Categorization Task. In: Barone, D., Teles, E., Brackmann, C. (eds) Computational Neuroscience. LAWCN 2017. Communications in Computer and Information Science, vol 720. Springer, Cham. https://doi.org/10.1007/978-3-319-71011-2_1
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