Extending the Neural Cellular Automata paradigm from pattern formation on grids to functional logic generation and self-repair on arbitrary graphs.

The genomic mechanisms that efficiently encode the initial architecture and synaptic connectivity of neural circuits remain poorly understood. We hypothesise that two primary mechanisms — spatial enco...

Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity. These low-dimensional representations h...

Spiking neurons underlie the brain’s extreme energy efficiency, and therefore have great potential in neuromorphic computing, although realising this efficiency in practice has proven challenging. We ...

A series of interactive workshops on building better experiment-theory collaborations in neuroscience.

Pengfei Sun et al. develop a spiking neural network with a dual memory pathway, co-designed with a custom neuromorphic chip. The approach delivers over 4× throughput and 5x energy efficiency gains whi...

The School of Convergence Science at Imperial College London is inviting applications for fully funded PhD studentships on research projects that sup...

Enlightening the brain

Kampff’s do-it-yourself approach inspired a generation of neuroscientists.

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...

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 ...

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...