Publication: Architectural Plasticity in Spiking Neural Networks

Architectural Plasticity in Spiking Neural Networks In Proceedings of The Artificial Life Conference 2020. MIT Press Montreal, Canada. 13-18 July 2020. Abstract— We can talk about learning optimisation in terms of three biological processes: evolution, development and learning. It has been argued that all three are necessary for intelligence to emerge. Together, they shape the brain … Continue reading Publication: Architectural Plasticity in Spiking Neural Networks

Publication: The Evolution of Training hyperparameters for SNNs with Hebbian Learning. (video)

The Evolution of Training Parameters for Spiking Neural Networks with Hebbian Learning In Proceedings of The Artificial Life Conference 2018. MIT Press Tokyo, Japan. 23-27 July 2018. Abstract— Spiking neural networks, thanks to their sensitivity to the timing of the inputs, are a promising tool for unsupervised processing of spatio-temporal data. However, they do not perform … Continue reading Publication: The Evolution of Training hyperparameters for SNNs with Hebbian Learning. (video)