The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
Brains process information in spiking neural networks. Their intricate connections shape the
diverse functions these networks perform. Yet how network connectivity relates to function is …
diverse functions these networks perform. Yet how network connectivity relates to function is …
Deep neuroethology of a virtual rodent
Parallel developments in neuroscience and deep learning have led to mutually productive
exchanges, pushing our understanding of real and artificial neural networks in sensory and …
exchanges, pushing our understanding of real and artificial neural networks in sensory and …
Neural manifold under plasticity in a goal driven learning behaviour
Neural activity is often low dimensional and dominated by only a few prominent neural
covariation patterns. It has been hypothesised that these covariation patterns could form the …
covariation patterns. It has been hypothesised that these covariation patterns could form the …
Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units
J Cornford, D Kalajdzievski, M Leite, A Lamarquette… - bioRxiv, 2020 - biorxiv.org
The units in artificial neural networks (ANNs) can be thought of as abstractions of biological
neurons, and ANNs are increasingly used in neuroscience research. However, there are …
neurons, and ANNs are increasingly used in neuroscience research. However, there are …
Recurrent neural network models of multi-area computation underlying decision-making
Cognition emerges from the coordination of computations in multiple brain areas. However,
elucidating these coordinated computations within and across brain regions is challenging …
elucidating these coordinated computations within and across brain regions is challenging …