  Information about external world is delivered to the brain in the form of
structured in time spike trains. During further processing in higher areas,
information is subjected to a certain condensation process, which results in
formation of abstract conceptual images of external world, apparently,
represented as certain uniform spiking activity partially independent on the
input spike trains details. Possible physical mechanism of condensation at the
level of individual neuron was discussed recently. In a reverberating spiking
neural network, due to this mechanism the dynamics should settle down to the
same uniform/periodic activity in response to a set of various inputs. Since
the same periodic activity may correspond to different input spike trains, we
interpret this as possible candidate for information condensation mechanism in
a network. Our purpose is to test this possibility in a network model
consisting of five fully connected neurons, particularly, the influence of
geometric size of the network, on its ability to condense information. Dynamics
of 20 spiking neural networks of different geometric sizes are modelled by
means of computer simulation. Each network was propelled into reverberating
dynamics by applying various initial input spike trains. We run the dynamics
until it becomes periodic. The Shannon's formula is used to calculate the
amount of information in any input spike train and in any periodic state found.
As a result, we obtain explicit estimate of the degree of information
condensation in the networks, and conclude that it depends strongly on the
net's geometric size.
