Mathematical Biology Seminar Robert Rosenbum, Notre Dame Wednesday, Nov. 14, 2018 3:05pm in LCB 225 Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks Abstract: Randomly connected networks of spiking neuron models in the asynchronous-irregular state provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. I will discuss recent work showing that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Using a Fokker-Planck formalism, we show that spatially extended spiking networks exhibit symmetry-breaking Turing-Hopf bifurcations to generate intricate spatiotemporal patterns. These dynamics can be trained to perform dynamical computations using a reservoir computing approach.