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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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"The Fabric of the Neocortex: Canonical Structure and Computations"
Andreas Tolias, Ph.D.
Associate Professor
Department of Neuroscience
Baylor College of Medicine
The neocortex is responsible for human perception, cognition and action, and its malfunction underlies numerous neuropsychiatric disorders. Despite major advances in our understanding of the functional properties of single neurons we still do not know how the cortex works at the circuit level. The essence of the problem lies in understanding how billions of neurons communicating through trillions of connections orchestrate their activities to give rise to our mental faculties. We are far from being able to simultaneously measure the activity of all the myriads of cortical cells and assemble their physical wiring diagram (whole brain connectome). However, if there are underlying principles and rules that govern this complexity, these principles could reduce the impenetrable complexity of the cortex to a manageable scale. One such principle is provided by the hypothesis that the cortex is composed of repeated elementary information processing modules, organized along cortical columns. We combine electrophysiological, imaging, and molecular tools with behavioral and machine learning approaches to determine what constitutes the elementary computational circuit motif in the neocortex and characterize its structure, function and decipher its canonical computation(s). I will describe our work towards those goals from three perspectives. First, from an anatomical perspective where we are mapping the detailed wiring diagram of the canonical cortical microcircuit including identifying all the cell types that comprise cortical circuits. Second, using electrophysiological and imaging methods we are characterizing the activity structure of large populations of neurons in the visual cortex during behavioral tasks. Third, we are using machine-learning methods to model these circuit motifs with the goal to decipher the canonical algorithm(s) they implement. In our work we use the macaque and mouse animal models, which we hope will ultimately enable us to understand the evolution of the neocortical motif at the structural and computational level.
