Aysel Sabuncu Brain Research Center Seminar: “Oscillatory Recurrent Gated Neural Integrator Circuits (ORGaNICs): A Unifying Theoretical Framework for Neural Dynamics,” Prof. David J. Heeger (New York University), C-Block Auditorium, 12:40PM November 4 (EN)

Aysel Sabuncu Brain Research Center Seminar

“Oscillatory Recurrent Gated Neural Integrator Circuits (ORGaNICs): A Unifying Theoretical Framework for Neural Dynamics”

Prof. David J. Heeger
New York University

Date/Time: Monday, November 4th, 12:40 pm
Place: C-Block amphitheater, FEASS building

Working memory is an example of a cognitive and neural process that is not static but evolves dynamically with changing sensory inputs; another example is motor preparation and execution. We introduce a theoretical framework for neural dynamics, based on oscillatory recurrent gated neural integrator circuits (ORGaNICs), and apply it to simulate key phenomena of working memory and motor control. The model circuits simulate neural activity with complex dynamics, including sequential activity and traveling waves of activity, that manipulate (as well as maintain) information during working memory. The same circuits convert spatial patterns of premotor activity to temporal profiles of motor control activity, and manipulate (e.g., time warp) the dynamics. Derivative-like recurrent connectivity, in particular, serves to manipulate and update internal models, an essential feature of working memory and motor execution. In addition, these circuits incorporate recurrent normalization, to ensure stability over time and robustness with respect to perturbations of synaptic weights. ORGaNICs can also be applied to model sensory processing, commensurate with the hypothesis that executive functions, motor preparation/control, and sensory processing share a common, canonical computational motif. Finally, these circuits can be implemented with a simplified biophysical (equivalent electrical circuit) model of pyramidal cells. Time permitting, I’ll also say a few words about inference, exploration, prediction, and the role of feedback (top-down) in the brain.

About the Speaker:
David J. Heeger’s research spans a cross-section of engineering, psychology, and neuroscience. Heeger holds a bachelor’s degree in mathematics as well as a master’s degree and doctorate in computer science—all from the University of Pennsylvania. He was a postdoctoral fellow at MIT, a research scientist at the NASA-Ames Research Center, and an associate professor at Stanford before joining NYU. Heeger has pioneering work in optic flow perception, image wavelet representation, and investigation of visual system using functional magnetic resonance imaging. He developed the normalization model of neuronal processing, and currently working on a new theory of cortical functioning. Heeger was awarded a number of prestigious prizes including the David Marr Prize in computer vision, and the Troland Research Award in psychology. He was elected to the National Academy of Sciences in 2013.