  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 bumpattractor 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.
