Evolving Spiking Neural Networks: Growth of Learning Machines

J. David Schaffer gave an excellent talk on evolving Spiking Neural Networks at the Center for Collective Dynamics of Complex Systems (CoCo) Seminar Series.

Many of today's neural network applications are based on multi-layer implementations of the perceptron. A perceptron implements a neuron model that sums weighted inputs and applies a non-linear activation function to calculate the output. Despite that this model deviates from how biological neuron networks work, the approach works and is used until today. In contrast, Spiking Neural Networks (SNNs) are a type of neural network that increase the realism in neural network simulation by introducing a time aspect into the model. Other than perceptron networks that decide upon their output at each propagation cycle, SNNs fire when a certain membrane potential is reached, which puts information into the timing of a spike.

J. David Schaffer shows how a genetic algorithm can be applied to generate an SNN for a given problem. Therefore, the chromosome representing an SNN will be mapped onto a binary string which will be evolved with mutation and recombination.

Evolving Spiking Neural Networks: Growth of Learning Machines from Complex Systems on Vimeo

Further readings on the topic:

• Evolutionary Computation at Wikipedia
• Spiking Neural Network at Wikipedia 
• J. David Schaffer's Webpage at Binghampton University
• R. Batllori, Craig B. Laramee, W. Land, J. David Schaffer. Evolving spiking neural networks for robot control. In Procedia Computer Science (6):329-334, 2011. (fulltext)
• I. Fehervari and W. Elmenreich. Evolving neural network controllers for a team of self-organizing robots. Journal of Robotics, 2010. (fulltext)
• I. Fehervari and W. Elmenreich. Evolution as a tool to design self-organizing systems. In Self-Organizing Systems, volume LNCS 8221, pages 139–144. Springer Verlag, 2014. (fulltext)
• I. Fehérvári, A. Sobe, and W. Elmenreich. Biologically sound neural networks for embedded systems using OpenCL. In Proceedings of the International Conference on NETworked sYStems (NETYS 2013). Springer Verlag, May 2013. (fulltext)
• J. David Schaffer, Darrell Whitley, Larry J Eshelman. Combinations of genetic algorithms and neural networks: A survey of the state of the art. International Workshop on Combinations of Genetic Algorithms and Neural Networks (COGANN-92), pages 1-37, 1992.