Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging.

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.

Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links.

Leaders of neuronal cultures in a quorum percolation model.

We present a theoretical framework using quorum percolation for describing the initiation of activity in a neural culture. The cultures are modeled as random graphs, whose nodes are excitatory neurons with k(in) inputs and k(out) outputs, and whose input degrees k(in) = k obey given distribution functions p(k). We examine the firing activity of the population of neurons according to their input degree (k) classes and calculate for each class its firing probability Φ(k)(t) as a function of t.