Memorization and Association on a Realistic Neural Model
Leslie Valiant, Harvard University
Friday, April 1, 2005 - 3:30 pm
* Wean Hall 7500 *


Abstract

A central open question of computational neuroscience is to identify the data structures and algorithms that are used in mammalian cortex to support successive acts of the basic cognitive tasks of memorization and association. This talk addresses the simultaneous challenges of realizing these two distinct tasks with the same data structure, and doing so while respecting the following four basic quantitative parameters of cortex, the neuron number, the synapse number, the synapse strengths, and the switching times. Previous work apparently had not succeeded in reconciling all these opposing constraints, the low values of synapse strengths that are typically observed experimentally having contributed a particular obstacle. We describe a computational scheme that supports both memory formation and association, and is feasible on networks of model neurons that respect the widely observed values of the above-mentioned four quantitative parameters. Our scheme allows for both disjoint and shared representations. The algorithms are simple, and in one version both memorization and association require just one step of neighborly influence. The issues of interference among the different circuits that are established, of robustness to noise, and of the stability of the hierarchical memorization process are addressed. A calculus, therefore, is implied for analyzing the capabilities of particular neural systems and subsystems, in terms of their basic numerical parameters.

Bio:

Leslie Valiant was educated at King's College, Cambridge, Imperial College, London; and at Warwick University where he received his Ph.D. in computer science in 1974. He is currently T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the Division of Engineering and Applied Sciences at Harvard, where he has taught since 1982. Before coming to Harvard he had taught at Carnegie-Mellon University, Leeds University, and the University of Edinburgh.

His work has ranged over several areas of theoretical computer science, particularly complexity theory, computational learning and parallel computation. He also works in computational neuroscience, where his interests are in understanding memory and learning.

He received the Nevanlinna Prize at the International Congress of Mathematicians in 1986 and the Knuth Award in 1997. He is a Fellow of the Royal Society (London) and a member of the National Academy of Sciences (USA).