Ph.D., Brown University
Associate Professor
Departments of Neuroscience and Applied Math
460 Sidney Frank Hall of Life Sciences
Tel: (401) 863-1195
Email: Lucien_Bienenstock@Brown.edu
Go to lab web page

The nature of the code(s) that our brains use to create, store, retrieve, matchand compose---and more generally compute with---neural representations ofexternal events, stimuli, sensations or actions still largely eludes us. Myresearch, carried out in close collaboration with several colleagues from theDepartments of Neuroscience, Applied Mathematics and Computer Science, attemptsto contribute to the understanding of brain codes, using a number ofmathematical/numerical tools. These range from the statistical analysis oflarge volumes of motor cortical activity recorded from behaving monkeys in thelaboratory of John Donoghue using state-of-the-art multielectrode arrays, tothe study of mathematical models of natural and artificial vision systems.

Our study of motor-cortex activity, in addition to contributing to a betterunderstanding of the fundamental principles underlying neural representationsof action (see Figure), has important practical applications. In particular, itallows us to devise more efficient and accurate computer algorithms tointerpret brain activity and transform it into the kinematic variables thatdescribe a complex arm movement. This is a crucial step in the building ofprosthetic devices, e.g. for patients paralyzed as a result of spinal cordinjury.

Our models of vision focus on the issues of invariant shape recognition andinterpretation of images that are locally ambiguous as are most images ofnatural scenes. We believe that the remarkable capacities of our brains atinterpreting such images are predicated on the use of compositional hierarchiesof explicit and detailed neural representations for objects/actions, theirparts, and the various relationships that exist between them. We activelyinvestigate, on both the theoretical and the experimental levels, thehypothesis that these representations are physically couched in the finetemporal structure of cortical activity, in particular in deviations fromstatistical independence of firing of distinct neurons manifested by an excessof synchrony measured on the millisecond scale.

Gao, Y., Black, M.J., Bienenstock, E., and Donoghue, J.P., 2001. Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex. Preprint.

Black, M. J., Bienenstock, E., Donoghue, J. P., Serruya, M., Wu, W., Gao, Y. (2003) Connecting brains with machines: The neural control of 2D cursor movement, 1st International IEEE/EMBS Conference on Neural Engineering, pp. 580-583.

Nicholas G. Hatsopoulos, Stuart Geman, Asohan Amarasingham, and Elie Bienenstock (2003) At what time scale does the nervous system operate? Neurocomputing 52-54, pp. 25-29.

Wei Wu, Michael J. Black, Yun Gao, Elie Bienenstock, Michael Serruya, Amar Shaikouni, and John P. Donoghue (2003) Neural Decoding of Cursor Motion using a Kalman Filter,  Advances in Neural Information Processing Systems 15, Suzanna Becker, Sebastian Thrun, and Klaus Obermayer, eds., MIT Press, pp 133-140.

Yun Gao, Michael J. Black, Elie Bienenstock, Shy Shoham and John P. Donoghue (2002) Probabilistic Inference of Arm Motion from Neural Activity in Motor Cortex,  Advances in Neural Information Processing Systems 14, Thomas G. Dietterich, Sue Becker, and Zoubin Ghahramani, eds., MIT Press, 213-220.

Bienenstock, E., Geman, S., and Potter, D., 1997. Compositionality, MDL Priors, and Object Recognition. In: Advances in Neural Information Processing Systems 9, M.C. Mozer, M.I. Jordan, and T. Petsche eds, MIT Press, pp 838-844.

Date, A., Bienenstock, E., and Geman, S., 1998. On the Temporal Resolution of Neural Activity. Technical Report, Division of Applied Mathematics, Brown University.