Critical Limits in a Bump Attractor Network of Spiking Neurons
Abstract:
A bump attractor network is a model that implements a competitive
neuronal process emerging from a spike pattern related to an input
source. Since the bump network could behave in many ways, this paper
explores some critical limits of the parameter space using various
positive and negative weights and an increasing size of the input
spike sources The neuromorphic simulation of the bump- attractor
network shows that it exhibits a stationary, a splitting and a
divergent spike pattern, in relation to different sets of weights and
input windows. The balance between the values of positive and negative
weights is important in determining the splitting or diverging
behaviour of the spike train pattern and in defining the minimal
firing conditions.