Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity The Developmental Neural Networks Workshop. The 2019 Conference on Artificial Life (ALife 2019). Newcastle, United Kingdom. 29 July - 2 August 2019. Abstract— Maintaining the ability to fire sparsely is crucial for information encoding in neural networks. Additionally, spiking homeostasis is vital … Continue reading Publication: Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity
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)
Wide Learning: Using an Ensemble of Biologically-Plausible Spiking Neural Networks for Unsupervised Parallel Classification of Spatio-Temporal Patterns In Proc. 2017 IEEE Symposium Series on Computational Intelligence (SSCI) Honolulu, Hawaii. 27 Nov-1 Dec 2017. Abstract— Spiking neural networks have been previously used to perform tasks such as object recognition without supervision. One of the concerns relating … Continue reading Publication: Wide Learning. SNN ensembles.