Spiking Neural Network Self-configuration for Temporal Pattern Recognition Analysis

In this work we provide design guidelines for the hardware implementation of Spiking Neural Networks. The proposed methodology is applied to temporal pattern recognition analysis. For this purpose the networks are trained using a simplified Genetic Algorithm. The proposed solution is applied to estimate the processing efficiency of Spiking Neural Networks.

[1]  Rainer Malaka,et al.  Solving nonlinear optimization problems using networks of spiking neurons , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[2]  Antoni Morro,et al.  A simple CMOS chaotic integrated circuit , 2008, IEICE Electron. Express.

[3]  Wulfram Gerstner,et al.  Spiking Neuron Models , 2002 .

[4]  Krzysztof J. Cios,et al.  Solving graph algorithms with networks of spiking neurons , 1999, IEEE Trans. Neural Networks.

[5]  Wulfram Gerstner,et al.  Spiking Neuron Models: An Introduction , 2002 .