Theories of structure, dynamics, and plasticity in neural circuits

Abstract

Neural circuits generate cognition, sensation, and behavior through the coordinated activity of many interconnected units. Understanding how these functions emerge dynamically and what connectivity structures support this emergence is a central challenge in neuroscience. This challenge is compounded by neural circuits' essential features: large numbers of components (neurons), nonlinear dynamics, complex recurrent interactions, and plastic connectivity. This thesis develops theoretical approaches to tackle this complexity, using tools from physics, particularly dynamical mean-field theory (DMFT), to analyze how connectivity structure shapes collective neuronal dynamics and computational functions in nonlinear recurrent neural networks.

David G. Clark

dgclark@fas.harvard.edu