Neural heterogeneity promotes robust learning

Nicolas Perez-Nieves (), Vincent C. H. Leung, Pier Luigi Dragotti and Dan F. M. Goodman ()
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Nicolas Perez-Nieves: Imperial College London
Vincent C. H. Leung: Imperial College London
Pier Luigi Dragotti: Imperial College London
Dan F. M. Goodman: Imperial College London

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract The brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that heterogeneity substantially improved task performance. Learning with heterogeneity was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks is similar to those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.

Date: 2021
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DOI: 10.1038/s41467-021-26022-3

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