Understanding distributed cognitive computations at a multi-regional scale
Ulises Pereira, Ph.D.
Swartz Fellow in Theoretical Neuroscience
New York University, Center for Neuroscience
Seminar abstract: Neuroscience is advancing at a breakneck pace into a big data era. Science consortiums, as well as large-scale lab collaborations, are producing ever-more-precise connectomes. In parallel, high-density probes now make it possible to record in the entire mouse brain during behavior by coordinating measurements across several labs. In physics, similar large-scale collaborations have been extremely successful. However, unlike physics, in which experimental discoveries go hand-to-hand with theoretical developments, in neuroscience, theories for multi-regional neural computations are seldom being developed. In this talk, I will present our recent results on modeling distributed cognitive computations in multi-regional brain circuits. I will start by describing a whole-macaque-cortex model for investigating distributed working memory. I will describe how spatial gradients of connectivity motifs integrated with connectomic and dendritic spine count data can be used to build a network model that recapitulates landmark observations in electrophysiological recordings. Then, I will demonstrate that similar principles can be used for building a model of the mouse cortico-basal ganglia-thalamocortical system that provides a framework for studying value-based decision-making. Finally, I will briefly discuss how local-circuit models for learning attractor and sequential activity can be integrated with connectomic and genetic data to study learning at a multi-regional scale.
Mechanisms underlying flexible information flow across the brain Karel Svoboda, Ph.D. Director, Allen Institute: Abstract: Neural computation and behavior are produced by shifting configurations of multi-regional neural networks, implemented by dynamic coupling between brain regions. We...