Understanding which features of network architecture are most important for shaping the collective activity patterns of neural circuits is a major goal of theoretical neuroscience. For example, what network properties influence critical exponents in neural circuits at critical points? Answering this question with theory requires tools from the renormalization group, which to date have not been applied to spiking network model used in neuroscience due to the all-or-nothing nature of spiking. Here we apply to RG to a stochastic spiking model and show that spontaneously active networks are controlled by a critical point in the Ising universality class.
Contact: Braden A. W. Brinkman, braden.brinkman@stonybrook.edu
Department of Neurobiology and Behavior & Center for Neural Circuit Dynamics, Stony Brook University, New York, USA
Presenter will not be available during poster viewing session, please direct all questions and comments to braden.brinkman@stonybrook.edu or leave them as a comment here