In this work, we study the dynamic range in a neural 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. The learning rules are related to neuroplasticity that describes change to the neural connections in the brain. 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.
|Pages (from-to)||1045 - 1052|
|Number of pages||8|
|Journal||Physica. A, Statistical Mechanics and its Applications|
|Early online date||24 Nov 2017|
|Publication status||Published - 15 Feb 2018|
- cellular automaton
- dynamic range