Spatial features of synaptic adaptation affecting learning performance
Neurons and Cognition
2017-09-21 v1 Disordered Systems and Neural Networks
Machine Learning
Neural and Evolutionary Computing
Abstract
Recent studies have proposed that the diffusion of messenger molecules, such as monoamines, can mediate the plastic adaptation of synapses in supervised learning of neural networks. Based on these findings we developed a model for neural learning, where the signal for plastic adaptation is assumed to propagate through the extracellular space. We investigate the conditions allowing learning of Boolean rules in a neural network. Even fully excitatory networks show very good learning performances. Moreover, the investigation of the plastic adaptation features optimizing the performance suggests that learning is very sensitive to the extent of the plastic adaptation and the spatial range of synaptic connections.
Cite
@article{arxiv.1709.06950,
title = {Spatial features of synaptic adaptation affecting learning performance},
author = {Damian L. Berger and Lucilla de Arcangelis and Hans J. Herrmann},
journal= {arXiv preprint arXiv:1709.06950},
year = {2017}
}