Efficiency and self-organized criticality in SNNs

Description

Spiking Neural Networks (SNNs) are artificial neural networks that more closely mimic biological neuronal functionalities: by processing visual information with binary, sparse and precisely-timed events (spikes), SNNs can process information faster and more efficiently when compared to traditional ANNs, and are thus ideally suited for processing spatio-temporal event-based information from neuromorphic sensors. However, SNNs are still difficult to train, mainly owing to their complex dynamics of neurons and the non-differentiable nature of spike operations. This project aims to address the issue of efficiency and activity normalization in large-scale SNNs, by making use of the theoretically well established phenomenon of self-organized criticality.

Period	2 Dec 2022
Event title	BioMorphic Intelligence Lab Kick-off Event and Symposium
Event type	Conference
Conference number	1
Location	Delft, NetherlandsShow on map
Degree of Recognition	National