Dendritic normalisation improves learning in sparsely connected artificial neural networks.

ABSTRACT: Artificial neural networks, taking inspiration from biological neurons, have become an invaluable tool for machine learning applications. Recent studies have developed techniques to effectively tune the connectivity of sparsely-connected artificial neural networks, which have the potential to be more computationally efficient than their fully-connected counterparts and more closely resemble the architectures of biological systems. We here present a normalisation, based on the biophysical behaviour of neuronal dendrites receiving distributed synaptic inputs, that divides the weight of an artificial neuron's afferent contacts by their number. We apply this dendritic normalisation to various sparsely-connected feedforward network architectures, as well as simple recurrent and self-organised n

SUBMITTER: Bird AD 

PROVIDER: S-EPMC8407571 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature