This work illustrates the application of a novel sequential
non-parametric method to detect significant changes of the functional
connectivity of neuronal networks using in-vivo multiple spike trains
recordings obtained via multi-electrode arrays. Rather than relying on
a parametric specification of the parameters' evolution, their
dynamics are assessed as a change-point problem in discrete time
within a hypothesis testing framework. The Kullback-Leibler divergence
between the posterior distributions of different sets of data under
the same model is proposed as a test statistic. Markov chain Monte
Carlo posterior simulation is used in general to approximate the value
of the Kullback-Leibler statistic and its critical region under the
null hypothesis of no change. The changes in the network parameters
detected by the Kullback-Leibler statistic explain variations of the
baseline neuronal spiking rates as well as of the functional
connections between neurons across different experiments.