Elie Bienenstock studies the mechanisms used by brains to create and compose complex representations. His research, focusing on models of vision, assumes that brains use compositional hierarchies of explicit and detailed representations of objects, parts, and relationships. With colleagues in neuroscience and applied math, he investigates the hypothesis that the fine temporal structure of cortical activity, e.g. the synchronous firing of neurons, plays an important role in these representations.

Biography

The first part of my research is in computer vision - how computers interpret images. I'm trying to contribute to the understanding of object recognition, which is part of artificial intelligence. Among its practical uses are military target recognition and optical character recognition in which an image is transformed into text. The main goal is to interpret highly ambiguous images, a task that is not solved very well by current algorithms. What characterizes our approach is that it is compositional. A face is made of eyes, nose, mouth, and each of these is made of simpler constituents, and there are rules that guide how these constituents come together in an image. We try to derive composition rules by studying natural images. We also try to learn the parameters of these rules by statistical methods. The other part of my research is in theoretical neuroscience. We are searching animal behavior data for specific temporal patterns that may shed light on the mechanisms used by the brain to construe separate parts into a whole object.

I love the interdisciplinary aspect of this research. I was attracted by both the mathematics and the neuroscience in it. I like to use my imagination to create theories that are abstract by their nature but can be tested.

Institutions

Brown University, 1980

Research Description

The nature of the code that our brains use to create, store, retrieve, match and compose—and more generally compute with—neural representations of external events, stimuli, sensations or actions still largely eludes us. My research, carried out in collaboration with colleagues from the Departments of Neuroscience, Applied Mathematics and Computer Science, attempts to contribute to the understanding of brain codes, using a number of mathematical/numerical tools. These range from the the study of mathematical models of natural and artificial vision systems to the statistical analysis of large volumes of cortical activity recorded from behaving monkeys, or the analysis or fMRI data recorded from humans engaged in various sensory-motor tasks.

Our models of vision focus on the issues of invariant shape recognition and interpretation of images that are locally ambiguous—as are most images of natural scenes. We believe that the remarkable capacities of our brains at interpreting such images are predicated on the use of compositional hierarchies of explicit and detailed neural representations for objects/actions, their parts, and the various relationships that exist between them. We actively investigate, on both the theoretical and the experimental levels, the hypothesis that these representations are physically couched in the fine temporal structure of cortical activity, in particular in deviations from statistical independence of firing of distinct neurons, manifested by an excess of synchrony measured on the millisecond scale.

Awards

N/A

Affiliations

Co-organizer: NATO Workshop on Disordered Systems and Biological Organization, Les Houches, France, March 1985.

Co-organizer: Interdisciplinary Workshop on Compositionality in Cognition and Neural Networks, Abbaye de Royaumont, France, May 1991.

Co-organizer: Interdisciplinary Workshop on Compositionality in Cognition and Neural Networks, Abbaye de Royaumont, France, June 1992.

Member, Fachbeirat (Scientific Advisory Board, with site visits), Max-Planck-Institut für Hirnforschung, Frankfurt, Germany.

Member, Comité National des Neurosciences (Scientific Advisory Board, with site visits throughout country), CNRS, France.

Referree for: Neural Computation; Neural Networks; Network-Computation in Neural Systems; Physica D; J. Neurophys.; Phys. Rev.; Phys. Rev. Lett.; IEEE Trans. on Neural Networks; Visual Neuroscience; International Conference on Artificial Neural Networks (ICANN); Journal of Computational Neuroscience.

Member, Editorial Board: Neural Networks; Network-Computation in Neural Systems.

Grant Reviewer: Israel Science Foundation; European Science Foundation; Ministére de la Recherche et de l'Education Nationale, France; Max-Planck Society, Germany; German-Israeli Science Foundation (GIF).

Review Panel on Collaborative Research in Computational Neuroscience, joint NSF-NIH program, Wachington DC, March 3-4, 2005.

Annual Site Visit of CELEST (Center of Excellence for Learning in Education, Science, and Technology ), NSF Science of Learning Center (SLC), Boston, June 19-21 2005.

Consulting: Industrial Automation: developed algorithms for widely used Optical-Character-Recognition software.

US Patent 10/455,509 "Method and System for Inferring Hand Motion From Multi-Cell Recordings in the Motor Cortex Using a Kalman Filter or Bayesian Model," Application filed 06/04/2003, Co-Inventor with Michael Black, Wei Wu, and Yun Gao.

Funded Research

NSF, Grant Number 0423031, CRCNS: Representation and Computation in Natural Vision, Sept. 2004 — Aug. 2007. PI: Stuart Geman. Co-PIs: Elie Bienenstock, David Sheinberg, Michael A. Paradiso.

NIH, Grant Number R01-NS050967, CRCNS: Learning the Neural Code for Prosthetic Control, Aug. 2004 — May 2007. PI: Michael Black. Co-PIs: Elie Bienenstock, Mayank Mehta, John Donoghue.

NINDS, Contract Number: N01–NS–2–2345 (PI: John Donoghue), "Cortical Control of Neural Prostheses," 09/30/02-09/29/05. Role: Co-Investigator.

NIH-NINDS, Grant Number: 2R37NS025074-14 (PI: John Donoghue), "Static and Dynamic Organization of Primate Cortex," July 1987—April 2006. Role: Co-Investigator.

DARPA, Contract Number MDA972-00-1-0026 (PI: Arto Nurmikko), "Coupling of Brain with microstructured electronic/ optoelectronic arrays: Interactive Computation at the
bio/info/micro interface" 7/17/00-7/16/05. Role: Co-Investigator

NIH-NINDS, Grant Number RO1-NS44834 (PI: Jerome Sanes), "Cognition and Action," 10/1/02-6/30/05. Role: Co-Investigator.

b. Completed Grants:

Human Frontier Sciences Project, Grant Number: RG 0103/1998B (PI Mark F. Bear), "The Synaptic Basis of Receptive Field Plasticity," 5/1/98-4/30/01, Co-Investigator.

NSF-ITR, Grant Number: EIA-0113679 (PI: Michael Black), "The Computer Science of Biologically-Embedded Systems," Sept. 2001 — Sept. 2004. Role: Co-PI.

Teaching Experience

TEACHING SINCE 1996

Statistical Analysis of Time Series, AM167, Spring 1996.

Mathematical Techniques for Neural Modeling, AM281-Neurosciences BN293, Fall 1996.

Operations Research: Probabilistic Models, AM120, Spring 1998, Enrollment: 22

M.Sc thesis, 1999: Asohan Amarasingham (Cognitive and Linguistic Sciences).

Statistical Analysis of Time Series, AM167, Spring 2000, Enrollment: 10

Short Course on Models of Brain Function, Center for Neural Computation, Hebrew University, Jerusalem, Israel, January 2000.

Methods of Applied Mathematics, II, AM34, Fall 2000, Enrollment: 14

Operations Research: Probabilistic Models, AM120, Spring 2001, Enrollment: 21

Statistical Analysis of Time Series, AM167, Fall 2001, Enrollment: 17

Computational Neuroscience, BN168, Spring 2002, Enrollment: 7

Statistical Inference I, AM165, Fall 2002, Enrollment: 61

Honors thesis 2002: Phoenix Kalen (Applied Mathematics).

Computational Neuroscience, BN168, Spring 2003, Enrollment: 12

Independent Study, AM196, Spring 2003, Enrollment: 1

Statistical Inference I, AM165, Fall 2003, Enrollment: 102

Computational Neuroscience, BN168, Spring 2004, Enrollment: 9

Research Talk Lab (co-taught with Andrea Simmons), Spring 2004, Enrollment: 9

Statistical Inference I, AM165, Fall 2004, Enrollment: 77

Independent Study, AM195, Fall 2004, Enrollment: 2

Computational Neuroscience, BN168, Spring 2005, Enrollment: 6

Statistical Inference I, AM165, Fall 2005, Enrollment: 99

Ph.D students:

René Doursat (Université Paris 6, France, June 1991)
Yun Gao (Applied Mathematics, Brown University, June 2004)
Anastasia Anishchenko (Physics, Brown University, November 2005)
Britt Anderson (Brain Science Program, Brown University, November 2005)

Courses Taught

• Computational Neuroscience (BN0168)
• Independent Study (AM0196)
• Independent Study (AM0195)
• Mathematical Techniques for Neural Modeling (AM0281)
• Methods of Applied Mathematics, II (AM0034)
• Operations Research: Probabilistic Models (AM0120)
• Statistical Analysis of Time Series (AM0167)
• Statistical Inference I (AM0165)

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