  Spatial relations between neurons in the network with time delays play a
crucial role in determining dynamics of the system. During the development of
the nervous system different types of neurons group together to enable specific
functions of the network. Right spatial distances, thus right time delays
between cells are crucial for an appropriate functioning of the system. To
model the process of neural migration we proposed simple but effective model of
network spatial evolution based on Hindmarsh-Rose neurons and
Metropoli-Hastings Monte Carlo algorithm. Under the specific assumptions and
using appropriate parameters of the neural evolution the network can converge
to the desirable state giving the opportunity of achieving large variety of
spectra. We show that there is a specific range of network size in space which
allows it to generate assumed output. A network or generally speaking a system
with time delays (corresponding to the arrangement in the physical space) of
specific output properties has a specific spatial dimension that allows it to
function properly.
