Neural Computations That Support Long Mixed Sequences of Knowledge Acquisition Tasks

Authors:
Leslie G. Valiant
Affiliations:
School of Engineering and Applied Sciences, Harvard University,
Venue:
TAMC '09 Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation
Year:
2009

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Abstract

In this talk we shall first give a brief review of a quantitative approach to understanding neural computation [4-6]. We target so-called random access tasks, defined as those in which one instance of a task execution may need to access arbitrary combinations of items in memory. Such tasks are communication intensive, and therefore the known severe constraints on connectivity in the brain can inform their analysis.