  In this paper we present a computational model which decodes the
spatio-temporal data from electro-physiological measurements of neuronal
networks and reconstructs the network structure on a macroscopic domain,
representing the connectivity between neuronal units. The model is based on
reservoir computing network (RCN) approach, where experimental data is used as
training and validation data. Consequently, the model can be used to study the
functionality of different neuronal cultures and simulate the network response
to external stimuli.
