Dynamics of neuron populations in noisy environments.

In this paper different topologies of populations of FitzHugh-Nagumo neurons have been introduced in order to investigate the role played by the noise in the network. Each neuron is subjected to an independent source of noise. In these conditions the behavior of the population depends on the connection among the elements. By analyzing several kinds of topology (ranging from regular to random) different behaviors have been observed. Several topologies behave in an optimal way with respect to the range of noise level leading to an improvement in the stimulus response coherence, while others with respect to the maximum values of the performance index. However, the best results in terms of both the suitable noise level and high stimulus response coherence have been obtained when a diversity in neuron characteristic parameters has been introduced and the neurons have been connected in a small-world topology.