This in turn implies that the ability of a single synapse to influence a neuron is likely to be inversely proportional to the total number of synapses that that neuron receives. We have recently shown that, in real neurons, this effect is precisely cancelled by the increased number of contacts allowed by longer dendrites. A neuron with longer dendrites is intrinsically less excitable as these currents can more easily dissipate both across the larger cell membrane and along the dendrites themselves. When a contact is formed, activity in one cell is communicated to another by altering the conductance of the receiving cell’s membrane and allowing an ionic current to flow. Neurons receive contacts from other cells on extensively branched processes known as dendrites. The results are two-fold, being both a practical advance in machine learning and an insight into how the structure of neuronal dendritic arbours may contribute to computation. The learning performance is significantly increased, providing an improvement over other widely-used normalisations in sparse networks. We apply this dendritic normalisation to various sparsely-connected feedforward network architectures, as well as simple recurrent and self-organised networks with spatially extended units.
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. 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. Artificial neural networks, taking inspiration from biological neurons, have become an invaluable tool for machine learning applications.
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