Neural networks rely on learning synaptic weights. However, this overlooks other neural parameters that can also be learned and may be utilized by the brain. One such parameter is the delay: the brain...

Spiking neural networks as universal function approximators (SNUFA) online workshop 2025. For more see http://snufa.net/2025/

Spiking Neural networks as Universal Function Approximators

In this NeuroView, Giacomo Indiveri discusses the origins and evolution of neuromorphic engineering, reflects on recent shifts toward digital and AI-centric approaches, and advocates for a revival of the field’s original principles, emphasizing analog, brain-inspired systems and the importance of fundamental neuroscience for future progress.

Spiking Neural networks as Universal Function Approximators

Neuromodulators allow circuits to dynamically change their biophysical properties in a context-sensitive way. In addition to their role in learning, neuromodulators have been suggested to play a role ...

Friedemann Zenke’s 2019 paper, and its related coding tutorial SpyTorch, made it possible to apply modern machine learning to spiking neural networks. The innovation reinvigorated the field.

Spiking Neural networks as Universal Function Approximators

Spiking Neural networks as Universal Function Approximators

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities ment...

The acoustic cues used by humans and other animals to localise sounds are subtle, and change during and after development. This means that we need to constantly relearn or recalibrate the auditory spa...

Scientific data and independence are at risk. We need to work with community-driven services and university libraries to create new multi-country organizations that are resilient to political…