UBER-HEB: Universal Biologically-inspired Environment for Research: Hierarchical Ersatz Brain

Award Information
Agency:
Department of Defense
Branch
Defense Advanced Research Projects Agency
Amount:
$99,000.00
Award Year:
2008
Program:
STTR
Phase:
Phase I
Contract:
W31P4Q-08-C-0433
Agency Tracking Number:
08ST1-0122
Solicitation Year:
2008
Solicitation Topic Code:
ST081-006
Solicitation Number:
2008.A
Small Business Information
APTIMA, INC.
12 Gill Street, Suite 1400, Woburn, MA, 01801
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
967259946
Principal Investigator:
Paul Allopenna
Senior Cognitive Scientist
(781) 496-2486
pallopenna@aptima.com
Business Contact:
Margaret Clancy
Chief Financial Officer
(781) 496-2415
clancy@aptima.com
Research Institution:
BROWN UNIV.
Michael Gaughan
Box 1929
164 Angell Street, 3rd Floor
Providence, RI, 2912
(401) 863-1799
Nonprofit college or university
Abstract
Decades of exponential advancement in computer capabilities have transformed the world, from toddlers’ playthings to the technologies of war and peace. Some further advances are constrained by the limits of von Neumann machines; in particular, they do not learn well. Aptima proposes to conduct a feasibility study to design “UBER-HEB,” a non-von Neumann hierarchical universal learning system. The work is grounded in the Ersatz Brain Project, a biologically-inspired architecture for cognitive computing, based on decades of research by Professor James Anderson of Brown University, a founder of neural networks. The team members have collaborated for a decade. The approach uses many sparsely connected modules inspired by cortical columns. Learning takes places via several integrated mechanisms: Hebbian, dynamic systems forming attractors, interference patterns of activations. It includes hierarchical structures and hierarchical properties also emerge naturally. It takes a biologically plausible and computationally powerful topographical approach to temporal encoding. We will structure the work around three test problems, for which we will develop running code. One of the problems concerns anomaly detection, which is of clear operational relevance. The feasibility assessment and resulting design will thus be based on both theoretical considerations and the results and analysis of the test problems.

* information listed above is at the time of submission.

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