  We study dynamics of a reverberating neural net by means of computer
simulation. The net, which is composed of 9 leaky integrate-and-fire (LIF)
neurons arranged in a square lattice, is fully connected with interneuronal
communication delay proportional to the corresponding distance. The network is
initially stimulated with different stimuli and then goes freely. For each
stimulus, in the course of free evolution, activity either dies out completely
or the network converges to a periodic trajectory, which may be different for
different stimuli. The latter is observed for a set of 285290 initial stimuli
which constitutes 83% of all stimuli applied. By applying each stimulus from
the set, we found 102 different periodic end-states. By analyzing the
trajectories, we conclude that neuronal firing is the necessary prerequisite
for merging different trajectories into a single one, which eventually
transforms into a periodic regime. Observed phenomena of self-organization in
the time domain are discussed as a possible model for processes taking place
during perception. The repetitive firing in the periodic regimes could underpin
memory formation.
