Dynamics of random neural networks with cell-type-specific connectivity

In neural circuits, statistical connectivity rules strongly depend on neuronal type. Here we study dynamics of neural networks with cell-type specific connectivity by extending the dynamic mean field method, and find that these networks exhibit a phase transition between silent and chaotic activity. By analyzing the locus of this transition, we derive a new result in random matrix theory: the spectral radius of a random connectivity matrix with block-structured variances.  We apply our results to show how a small group of hyper-excitable neurons within the network can significantly increase the network’s computational capacity.