Neuroevolution of Spiking Neural Networks Using Compositional Pattern Producing Networks

Elbrecht, Daniel; Schuman, Catherine

doi:10.1145/3407197.3407198

Neuroevolution of Spiking Neural Networks Using Compositional Pattern Producing Networks

Conference · Wed Jul 01 00:00:00 EDT 2020

DOI:https://doi.org/10.1145/3407197.3407198· OSTI ID:1649077

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ORNL

Spiking neural networks (SNNs) offer tremendous potential for the future of AI, including the ability to be implemented efficiently on neuromorphic systems. One of the challenges in building functioning SNNs is the training process, as standard error back-propagation cannot be easily applied. In this work, we extend an evolutionary approach for training SNNs by implementing an indirect encoding of individuals. Specifically, we evolve SNNs using Compositional Pattern Producing Networks, which are able to learn the connectivity patterns between neurons defined in a coordinate space. We validate the approach on multiple control and classification tasks.

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1649077

Country of Publication:: United States

Language:: English

References (6)

Spike Timing-Dependent Plasticity of Neural Circuits Dan, Yang; Poo, Mu-ming Neuron, Vol. 44, Issue 1 https://doi.org/10.1016/j.neuron.2004.09.007	journal	September 2004
Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke Kasabov, Nikola; Feigin, Valery; Hou, Zeng-Guang Neurocomputing, Vol. 134 https://doi.org/10.1016/j.neucom.2013.09.049	journal	June 2014
Evolving Neural Networks through Augmenting Topologies Stanley, Kenneth O.; Miikkulainen, Risto Evolutionary Computation, Vol. 10, Issue 2 https://doi.org/10.1162/106365602320169811	journal	June 2002
An evolutionary optimization framework for neural networks and neuromorphic architectures Schuman, Catherine D.; Plank, James S.; Disney, Adam 2016 International Joint Conference on Neural Networks (IJCNN) https://doi.org/10.1109/IJCNN.2016.7727192	conference	July 2016
Hardware spiking neural network prototyping and application Cawley, Seamus; Morgan, Fearghal; McGinley, Brian Genetic Programming and Evolvable Machines, Vol. 12, Issue 3 https://doi.org/10.1007/s10710-011-9130-9	journal	April 2011
First-Spike-Based Visual Categorization Using Reward-Modulated STDP Mozafari, Milad; Kheradpisheh, Saeed Reza; Masquelier, Timothee IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, Issue 12 https://doi.org/10.1109/TNNLS.2018.2826721	journal	December 2018

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