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 ENG/EFRI FY 2008 Awards Announcement

This story is from the category The Brain
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Date posted: 05/10/2008

Earlier today, we looked at New Research to Open Neuro Frontiers, in which the US National Science Foundation announced a slew of grants for brain and neuroprosthetic research. The projects to recieve funding are as follows:

Creating a learning algorithm of the brain
The project ?Deep learning in the mammalian visual cortex? (grant #0835878) will be led by Andrew Ng of Stanford, in collaboration with Ed Boyden of Massachusetts Institute of Technology (MIT), Yann LeCun of New York University, and Yang Dan of the University of California, Berkeley.

This project will employ high-performing artificial neural network systems, new models of deep learning from cognitive science, and new experiments on the visual cortex to begin integrating what is known about the challenging task of recognizing objects from visual inputs. The research will involve decisive experiments to test assumptions about local feedback in the learning system, the results of which may encourage new computational models of the brain.

Studying neural networks with an innovative patch-clamp array
The project ?Dynamics of neural networks on a planar patch-clamp array: training, identification, and control? (0835947) will be led by Russell Tedrake of MIT, in collaboration with Alexandre Megretski and H. Sebastian Seung of MIT and Hongkun Park of Harvard University.

This research has the potential to revolutionize in vitro work on cells by solving technical problems with planar patch-clamp arrays. (Patch-clamp arrays are tiny electrodes placed within cells that can describe and control certain cell activities.) If successful, the new patch-clamp arrays will be able to monitor hundreds of cells effectively in parallel, a major step towards interfacing with hundreds of neurons in the brain itself.

A second goal is to develop new, simplified models of living neural circuits and to train these circuits to address benchmark challenges that represent the cutting edge of robotics research.

Determining how the brain controls the hand
The project ?Reverse-engineering the human brain?s ability to control the hand? (grant #0836042) will be led by Francisco Valero-Cuevas of the University of Southern California, in collaboration with Chang Liu of Northwestern University, Yoky Matsuoka of the University of Washington, and Emanuel Todorov of the University of California, San Diego.

The main goal of this project is to understand how to achieve dexterous, approximately optimal control of a hand by having humans and computers perform familiar but challenging tasks of manipulating objects. Researchers will use the same algorithms both to model human motor control and to go beyond the present state of the art in robotic manipulation. Dexterous robotic hands have a wide variety of possible applications in industry, space, and national security. Improved understanding of how humans learn to optimize hand performance will also have broader benefits, particularly for the disabled.

Modeling control of the electric power grid on the brain
The project ?Neuroscience and neural networks for engineering the future intelligent electric power grid? (grant #0836017) will be led by Ganesh Venayagamoorthy of the Missouri University of Science and Technology (Missouri S&T, formerly University of Missouri-Rolla), in collaboration with Donald Wunsch of Missouri S&T, and Ronald Harley and Steve Potter of Georgia Institute of Technology.

While previous work on living neural networks (LLNs) has focused on challenges like managing a single control variable, electric power grids entail thousands of interconnected variables that must be managed in real-time. The new work in vitro will probe the ability of LNNs made up of thousands of cells to predict the behavior of a complicated power grid simulator, and it will test the ability of new biological learning models to explain their capabilities. New mathematical concepts for how to cope with such complexity will also be tested by addressing the same prediction challenge, and by attempting to apply adaptive, anticipatory control for the first time to large-scale power grid control in simulation.

See the full Story via external site: www.nsf.gov



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