A Spike Neural Network Design Approach to Reduce Parameters for Evolving Signal Classifiers

Provided by: International Journal of Engineering and Technology
Topic: Networking
Format: PDF
"Spiking neural networks have powerful appeal when it comes to spatio-temporal pattern detection tasks, due to their implicit nature of accepting as well as processing temporally encoded information. However, achieving the dream has proven rather challenging with at least one factor being that the number of tunable parameters grows significantly with the network size. In this paper, the authors offer design approaches for two network types, a sequence detector (multi-channels) and a temporal pattern detector (single channel), that detect predefined Inter-Spike Interval (ISI) patterns in spike trains. The network itself does not learn; it has no need to, the network is correct by design."

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