Pattern recognition using a recurrent neural network inspired on the olfactory bulb

Authors:
Lucas Baggio Figueira;Antonio Carlos Roque
Affiliations:
Laboratory of Neural Systems, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, SP, Brazil;Laboratory of Neural Systems, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, SP, Brazil
Venue:
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Year:
2011

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Abstract

The olfactory system is a remarkable system capable of discriminating very similar odorant mixtures. This is in part achieved via spatio-temporal activity patterns generated in mitral cells, the principal cells of the olfactory bulb, during odor presentation. In this work, we present a spiking neural network model of the olfactory bulb and evaluate its performance as a pattern recognition system with datasets taken from both artificial and real pattern databases. Our results show that the dynamic activity patterns produced in the mitral cells of the olfactory bulb model by pattern attributes presented to it have a pattern separation capability. This capability can be explored in the construction of high-performance pattern recognition systems.