Towards evolving spiking networks with memristive synapses

By Gerard David Howard, Ella M. Gale, Larry Bull, B. D. L. Costello, A. Adamatzky. 2011

16 citations recorded by the source corpus.

This paper presents a spiking neuro-evolutionary system which implements memristors as neuromodulatory connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and a constructionist approach, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to be evolved for each network. We demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We evaluate two phenomenological real-world memristive implementations against a theoretical “linear memristor”, and a system containing standard connections only. Our networks are evaluated on a simulated robotic navigation task.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

Citations in the corpus

• Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, neutral): “Howard et al. (2011, 2012, 2014) devised experiments to evolve plastic spiking networks implemented as memristors for simulated robotic navigation tasks.”
• A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017, unknown): “[547], [735], [1077], [1329]–[1333], [1339], [1503], [1539],”