  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.
