  Neuronal dynamics are fundamentally constrained by the underlying structural
network architecture, yet much of the details of this synaptic connectivity are
still unknown even in neuronal cultures in vitro. Here we extend a previous
approach based on information theory, the Generalized Transfer Entropy, to the
reconstruction of connectivity of simulated neuronal networks of both
excitatory and inhibitory neurons. We show that, due to the model-free nature
of the developed measure, both kinds of connections can be reliably inferred if
the average firing rate between synchronous burst events exceeds a small
minimum frequency. Furthermore, we suggest, based on systematic simulations,
that even lower spontaneous inter- burst rates could be raised to meet the
requirements of our reconstruction algorithm by applying a weak spatially
homogeneous stimulation to the entire network. By combining multiple recordings
of the same in silico network before and after pharmacologically blocking
inhibitory synaptic transmission, we show then how it becomes possible to infer
with high confidence the excitatory or inhibitory nature of each individual
neuron.
