DescriptionSpiking 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|
|Degree of Recognition||National|
BioMorphic Intelligence Lab Kick-off Event and Symposium
Press/Media: Public Engagement Activities