Advisory Board

Dr. Garrett T. Kenyon

The ScienceDaily article World-record Supercomputer Mimics Human Sight Brain Mechanisms said

Less than a week after Los Alamos National Laboratory’s Roadrunner supercomputer began operating at world-record petaflop-per-second data-processing speeds, Los Alamos researchers are already using the computer to mimic extremely complex neurological processes.
Welcome to the new frontier of research at Los Alamos: science at the petascale.
The prefix “peta” stands for a million billion, also known as a quadrillion. For the Roadrunner supercomputer, operating at petaflop/s performance means the machine can process a million billion calculations each second. In other words, Roadrunner gives scientists the ability to quickly render mountainous problems into mere molehills, or model systems that previously were unthinkably complex.
The PetaVision Synthetic Cognition team responsible for the theory and codes run on Roadrunner includes: Luis Bettencourt, Garrett Kenyon, Ilya Nemenman, John George, Steven Brumby, Kevin Sanbonmatsu, and John Galbraith, all of Los Alamos; Steven Zuker of Yale University; and James DiCarlo from Massachusetts Institute of Technology.

Garrett T. Kenyon, Ph.D. is Technical Staff Member, Biological and Quantum Physics (P-21), Los Alamos National Laboratory (LANL).
His research topics include:
Extreme Synergy: Recent results at Princeton indicate that even relatively weak pairwise correlations between retinal neurons can, in aggregate, produce astonishing levels of order in the resulting firing patterns, in much the same way that the interactions between local domains can lead to global ordering of ferromagnetic materials below the Curie temperature. Garrett has recently been investigating the hypothesis that the presence of similarly realistic pairwise correlations could allow downstream targets to more rapidly reconstruct visual stimuli from retinal spike trains.
His findings, reported in a preprint entitled Extreme Synergy, suggest that information regarding the local intensity of each pixel can in many cases be widely distributed across a large population containing hundreds of retinal ganglion cells all responding to the same contiguous stimulus, a non-local encoding strategy that may have evolved to minimize the number of spikes necessary to support rapid image reconstruction.
Self-Repairing Synapses: One of the great unsolved mysteries of neuroscience is how we are able to retain long-term memories despite the known volatility of individual synapses, which are subject to ongoing random changes due to both intrinsic and extrinsic sources. He suggests that a robust solution to this problem requires a fundamental reassessment of what types of information can and cannot be learned by biological systems.
Specifically, he suggests that the decision surfaces maintained by any given pattern of synaptic weights, in order to remain stable under random fluctuations, must correspond to separable independent components in the raw environmental input. The problem of storing memories over long periods, despite random fluctuations in individual synaptic weights, can thus be solved by exploiting the structure present in the environment itself. As a corollary, his findings suggest that in a purely random environment, long-term storage of information would be impossible.
High-Performance Neural Computing: Simulating large, semi-realistic neural systems will clearly require massive computational resources. He is developing a suite of object-oriented tools that will allow any neural simulator to maximum advantage of high-end computer clusters.
Garrett authored Extreme Synergy in a Retinal Code: Spatiotemporal Correlations Enable Rapid Image Reconstruction and A model of long-term memory storage in the cerebellar cortex: A possible role for plasticity at parallel fiber synapses onto stellate/basketinterneurons, and coauthored A Model of high frequency oscillatory potentials in retinal ganglion cells, Effects of firing synchrony on signal propagation in layered networks, Correlated Firing Improves Stimulus Discrimination in a Retinal Model, and A Mathematical Model of the Cerebellar-Olivary System II: Motor Adaptation Through Systematic Disruption of Climbing Fiber Equilibrium.
Garrett earned his B.A. in Physics at the University of California at Santa Cruz in 1984, his M.S. in Physics at the University of Washington in 1986, and his Ph.D. in Physics at the University of Washington in 1990.