TY  - INPR
A1  - Bird, Alexander D.
A1  - Cuntz, Hermann
T1  - Dendritic normalisation improves learning in sparsely connected artificial neural networks
T2  - bioRxiv
N2  - Inspired by the physiology of neuronal systems in the brain, artificial neural networks have become an invaluable tool for machine learning applications. However, their biological realism and theoretical tractability are limited, resulting in poorly understood parameters. We have recently shown that biological neuronal firing rates in response to distributed inputs are largely independent of size, meaning that neurons are typically responsive to the proportion, not the absolute number, of their inputs that are active. Here we introduce such a normalisation, where the strength of a neuron’s afferents is divided by their number, to various sparsely-connected artificial networks. The learning performance is dramatically increased, providing an improvement over other widely-used normalisations in sparse networks. The resulting machine learning tools are universally applicable and biologically inspired, rendering them better understood and more stable in our tests.
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/72761
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-727616
IS  - 2020.01.14.906537
ER  -