In this work, we study the dynamic range in a neuronal network modelled by cellular automaton. We consider deterministic and non-deterministic rules to simulate electrical and chemical synapses. Chemical synapses have an intrinsic time-delay and are susceptible to parameter variations guided by learning Hebbian rules of behaviour. Our results show that chemical synapses can abruptly enhance sensibility of the neural network, a manifestation that can become even more predominant if learning rules of evolution are applied to the chemical synapses.
@article{arxiv.1709.02719,
title = {How synapses can enhance sensibility of a neural network},
author = {P. R. Protachevicz and F. S. Borges and K. C. Iarosz and I. L. Caldas and M. S. Baptista and R. L. Viana and E. L. Lameu and E. E. N. Macau and A. M. Batista},
journal= {arXiv preprint arXiv:1709.02719},
year = {2018}
}