Guaranteed to be more fun than unboxing, come to the talk and be illuminated!
Michael
TITLE Don’t paint the box black: Using dynamical systems to understand the high-dimensional activity of cortex-inspired neural networks
KEYWORDS biological NNs, learning dynamics, systems analysis, statistical physics
ABSTRACT
The dynamics of cortical circuits are thought to be responsible for much of the learning in higher vertebrates. Over the last decades, a data-driven theoretical effort in neuroscience has sought to understand the collective dynamics of 'task-ready' cortical activity. In this talk, I will first recount the work that led up to the first self-consistent mean-field theory of such activity in 2010. Employing a long-standing framework from dynamical systems and statistical physics suited for characterizing information processing in high-dimensional dynamics, subsequent work has focussed on understanding the properties of the underlying full spiking network dynamics. I will describe some of these approaches, and present original work that analytically treats the complex phase space geometry of a multi-stable regime exhibited by these models. The outlook for this general framework looks bright as we more broadly seek to elucidate high-dimensional learning dynamics in a variety of systems, both biological and machine.
BIOMaximilian Puelma Touzel is a theoretical biophysicist interested in understanding how learning systems build and adapt representations of their environment. He studied math and physics at the University of Toronto and then theoretical neuroscience at the Max Planck Institute for Dynamics and Self-Organization in Göttingen, Germany, receiving a PhD there under the supervision of Fred Wolf. Currently, he is a post-doctoral fellow working on inference of probabilistic models of immune repertoire change with Aleksandra Walczak and Thierry Mora in the Laboratoire de Physique Théorique at the Ecole Normale Supérieure in Paris.