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...

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A new tool that blends your everyday work apps into one. It's the all-in-one workspace for you and your team

A framework for training artificial neural networks in physical space allows neuroscientists to build networks that look and function like real brains.

A fundamental question in neuroscience is what are the constraints that shape the structural and functional organization of the brain. By bringing biological cost constraints into the optimization pro...

medRxiv - The Preprint Server for Health Sciences

The principles underpinning the projection patterns of the thalamus to the cortex are not well characterised. Here, the authors reveal a medial to lateral thalamic gradient of genetic and co...

Associative Memory & Hopfield Network Worshop @NeurIPS 2023. Discuss the latest multidisciplinary developments in Associative Memory and Hopfield Networks. Imagine new tools built around memory as com...

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism…

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

There is little doubt that the PFC is important for WM [1]. Some of the earliest studies in nonhuman primates demonstrated that the structural [2] and functional [3] integrity of the PFC is necessary ...

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Most learning algorithms in machine learning rely on gradient descent to adjust model parameters, and a growing literature in computational neuroscience leverages these ideas to study synaptic...