How synapses can enhance sensibility of a neural network

Abstract

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.

References in corpus (3)

• Signal integration enhances the dynamic range in neuronal systems
• Complementary action of chemical and electrical synapses to perception
• Effects of the spike timing-dependent plasticity on the synchronisation in a random Hodgkin-Huxley neuronal network