Structure and Dynamics of Cultured Neuronal Networks

We have developed a method that can reconstruct the connectivity structure of a weighted directed network using only time-series measurements of the dynamics of the nodes. Our method is guided by noise-induced mathematical relations. We apply this method to reconstruct cultured neuronal networks using the electrical signals recorded in cultures of cortices of rat embryos by multi-electrode arrays. The reconstructed neuronal networks have 4095 nodes; their connection probability and proportion of the giant strongly connected component are comparable to those of the chemical synapse network of C. Elegans, and both are small-world networks. Our method can further reconstruct the average incoming and outgoing coupling strength of each node and whether the nodes are excitatory or inhibitory. We obtain various interesting results about the distributions of the in-degree and out-degree, and the average incoming and outgoing coupling strength of the nodes. Moreover, we find that the spike rate of the nodes is related to their network properties. Using this relation, we can predict whether a node has high or low spike rate with high accuracy.