Computational Neuroscience

This program unit fosters research to elucidate the organization, structural bases and operational algorithms characterizing information-processing networks within neural systems. The goal is the development of biological neural networks that incorporate the organizational principles and operational rules of real nervous systems that provide demonstrable enhancements in the capability of information processing systems. Research supported includes neural microcircuitry, in particular from cortical networks, and sensorimotor systems composed of multiple networks. The interest in microcircuitry is aimed at elucidating the principles of neural structure-function relationships, and identifying those aspects of connectivity, neural biophysics and network dynamics that enable scalable, powerful and efficient neural computation.

The program's current priority is development of large-scale cortical models, possibly embedded within larger neural systems, with demonstrable computational ability. The goal is to develop large-scale neocortical models with capabilities extending beyond pattern recognition into the domain of cognitive skills. New brain imaging technologies are providing important data on the neural substrates of cognition at the meso-scale provide an opportunity to bridge neuroscience and cognitive science accounts of cognitive skills. Interdisciplinary approaches that combine cognitive neuroscience and neural modeling based on biological principles are of particular interest.

Program Contact Information

Name: Dr. Tom McKenna

Title: Program Manager

Department: Code 34

Division: Human and Bioengineered Systems


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