Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Pulsed Neural Networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Synapses as dynamic memory buffers
Neural Networks
Applications of spiking neural networks
Information Processing Letters - Special issue on applications of spiking neural networks
A series of failed and partially successful fitness functions for evolving spiking neural networks
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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We are interested in engineering smart machines that enable backtracking of emergent behaviors. Our SSNNS simulator consists of hand-picked tools to explore spiking neural networks in more depth with flexibility. SSNNS is based on the Spike Response Model (SRM) with capabilities for short and long term memory. A genetic algorithm, namely CHC, is used independently to generate such example systems that produce patterns of interest. Foundational work in the growing field of spiking neural networks has shown that precise spike timing may be biologically more plausible and computationally powerful than traditional rate-based models[4][7]. We have been using evolution to discover neural configurations that produce patterns of interest.