Rajesh P. N. Rao

Research Interests:

Visit the Laboratory for Neural Systems Web Page for a list of ongoing projects.

News from the Laboratory for Neural Systems

Recent professional activities
Gordon Research Conference on Theoretical Biology, invited speaker, 2006
Workshop on Bayesian Cognition, invited speaker, 2006
Computer Vision and Pattern Recognition (CVPR), program committee, 2006
Probabilistic Models of Cognition Workshop, invited speaker, 2005
Okinawa Computational Neuroscience Course, co-organizer, 2004
ICDL program committee, 2004
AAAI-04 program committee, 2004
Mathematical Neuroscience Workshop, invited speaker, 2004
Workshop on STDP, invited speaker, 2004
NIPS program committee, 2003
Sloan-Swartz Annual Meeting on Theoretical Neurobiology, invited speaker, 2003
AISB Convention, keynote speaker, 2003
Neural Information and Coding Workshop, invited speaker, 2003
MBI Workshop on Systems-Level Modeling/Neural Coding, invited speaker, 2002/2003
NIPS organizing committee, 2002
Meeting with the President of India, August 30, 2002.

Awards

• ONR Young Investigator Award, 2003-2006
• David and Lucile Packard Fellowship, 2002-2007
• NSF CAREER Award, 2002-2007
• Alfred P. Sloan Research Fellowship for Junior Faculty, 2001-2003
• Alfred P. Sloan Postdoctoral Fellowship, 1997-2000

Publications

Books

• 
  Bayesian Brain: Probabilistic Approaches to Neural Coding (co-edited volume, MIT Press, 2007)
• 
  Probabilistic Models of the Brain (co-edited volume, MIT Press, 2002)
  • Review by Josh McDermott (Nature Neurosci. 5(9), 829, 2002)

Selected Articles

• Planning and Acting in Uncertain Environments using Probabilistic Inference (with D. Verma, Proceedings of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2006)
• Dynamic imitation in a humanoid robot through nonparametric probabilistic inference (with Grimes and Chalodhorn, Proceedings of the 2006 Robotics Science and Systems Conference, 2006)
• Electrocorticography-based brain computer interface -- the Seattle experience (with Leuthardt et al., IEEE Trans Neural Syst Rehab Eng 14(2):194-8, 2006)
• A probabilistic model of gaze imitation and shared attention (with Hoffman et al., Neural Networks 19(3):299-310, 2006)
• Towards adaptive classification for BCI (with Shenoy et al., J Neural Eng 3(1):R13-23, 2006)
• Learning Dynamic Humanoid Motion using Predictive Control in Low Dimensional Subspaces, Chalodhorn, R., Grimes, D. B., Maganis, G. and Rao, R. P. N., Proceeding of the IEEE-RAS/RSJ International Conference on Humanoid Robots, Dec 2005 (Humanoids 2005)
• Learning Humanoid Motion Dynamics through Sensory-Motor Mapping in Reduced Dimensional Spaces, Chalodhorn, R., Grimes, D. B., Maganis, G., Rao, R. P. N., and Asada, M., Proceeding of the IEEE International Conference on Robotics and Automation, May 2006 (ICRA 2006)
• Neural models of Bayesian belief propagation (Bayesian Brain, 2007)
• Bayesian inference and attention in the visual cortex (Neuroreport 16(16), 1843-1848, 2005)
• Goal-based imitation as probabilistic inference over graphical models (Advances in NIPS 18, 2006)
• Learning shared latent structure for image synthesis and robotic imitation (Advances in NIPS 18, 2006)
• Hierarchical Bayesian inference in networks of spiking neurons (Advances in NIPS 17, 2005)
• Dynamic Bayesian networks for brain-computer interfaces (with P. Shenoy, Advances in NIPS 17, 2005)
• Adaptive myographic control of a robotic arm (Proceedings of AAAI, 523-528, 2005)
• Bilinear sparse coding for invariant vision (with D. Grimes, Neural Computation, 17(1), 47-73, 2005)
• Bayesian computation in recurrent neural circuits (Neural Computation, 16(1), 1-38, 2004)
• Graphical models for planning and imitation (with D. Verma, Technical Report 2005-02-01, February 2005)
• A Bayesian model of imitation in infants and robots (with A. Shon and A. Meltzoff, Imitation and Social Learning in Robots, Humans, and Animals, Cambridge University Press, 2005)
• A probabilistic framework for model-based imitation learning (with A. Shon, D. Grimes, and C. Baker, Proceedings of the 26th Annual Meeting of the Cognitive Science Society, 2004)
• Probabilistic models of attention based on iconic representations and predictive coding (with Dana Ballard, Neurobiology of Attention, 2004)
• Probabilistic bilinear models for appearance-based vision (with David Grimes and Aaron Shon, Proc. of ICCV'03, 2003)
• Self-organizing neural systems based on predictive learning (with Terry Sejnowski, Phil. Trans. Royal Soc. Lond. A, Vol. 361, 2003)
• Imitation learning in infants and robots (with Andy Meltzoff, Proc. of AISB'03, 2003)
• A Bilinear Model for Sparse Coding (with D. Grimes, Advances in NIPS 15, 2003)
• Learning Temporal Patterns by Redistribution of Synaptic Efficacy (with A. Shon, Neurocomputing, 2003)
• Receptive Field (invited review article) (Encyclopedia of the Human Brain, 2002)
• Models of Attention (invited review article) (Encyclopedia of Cognitive Science, 2002)
• Eye Movements in Iconic Visual Search (Vision Research 42(11), 1447-1463, 2002)
• Awakening a sleeping cat: Invited review of Information Theory and the Brain (Neural Networks, 2002)
• Spike Timing Dependent Hebbian Plasticity as Temporal Difference Learning (Neural Computation, 13(10), 2221-2237, 2001)
  • News and Views by Peter Dayan (Trends in Cog. Sci., 6(3), 105-106, 2002)
• Optimal Smoothing in Visual Motion Perception (Neural Computation, 13(6), 1243-1253, 2001)
• Predictive Learning of Temporal Sequences in Neocortical Circuits (Complexity in Bio. Info. Processing, 2001)
• Reliability of Spike Timing in Cortical Neurons (J. Neurophysiology, 85(4), 1782-1787, 2001)
• Neural Circuits in Silicon [News & Views] (Nature, 405, 891-892, 2000)
  • Postscript file
  • New York Times article
• Predictive Sequence Learning in Recurrent Neocortical Circuits (Advances in NIPS 12, 164-170, 2000)
• Learning to Maximize Rewards: Review of the book "Reinforcement Learning" (Neural Networks, 13(1), pp. 135-137, 2000)
• Predictive Coding in the Visual Cortex (Nature Neuroscience, 2(1), 79-87, 1999)
  • Paper (PDF format)
  • News and Views by Koch & Poggio (PDF format)
  • News article in Nature
• An Optimal Estimation Approach to Visual Perception and Learning (Vision Research, 39(11), 1963-1989, 1999)
• Learning Lie Groups for Invariant Visual Perception (Advances in NIPS 11, pp. 810-816, 1999)
• What/Where Networks & Local Receptive Fields for Transformation Estimation (Network, 9(2), pp. 219-234, 1998)
• Visual Attention during Recognition (Advances in NIPS 10, pp. 80-86, 1998)
• Kalman Filter Model of the Visual Cortex (Neural Computation, 9(4), pp. 721-763, 1997)
• Deictic Codes for the Embodiment of Cognition (Behav. and Brain Sciences, 20(4), pp. 723-767, 1997)

Teaching:

• CSE 599E: Brain-Computer Interfaces (BCI) (Spring 2006)

• CSE 322: Introduction to Formal Models in Computer Science (Winter 2006)

• CSE/NEUBEH 528: Computational Neuroscience (Winter 2005)

• CSE 322: Introduction to Formal Models in Computer Science (Autumn 2004)

• CSE 322: Introduction to Formal Models in Computer Science (Spring 2004)

• CSE 590RR: Introduction to Computational Neuroscience (Autumn 2003)

• CSE 590NC: Neural Computation Seminar (Spring 2003)

• CSE 590NC: Neural Computation Seminar (Winter 2003)

• CSE 326: Data Structures and Algorithms (Winter 2003)

• CSE 590NC: Neural Computation Seminar (Autumn 2002)

• CSE 322: Introduction to Formal Models in Computer Science (Autumn 2002)

• CSE 590RR: Introduction to Computational Neuroscience (Spring 2002)

• CSE 590NC: Neural Computation Seminar (Winter 2002)

• CSE 322: Introduction to Formal Models in Computer Science (Autumn 2001)

• CSE 590NC: Neural Computation Seminar (Autumn 2001)

• CSE 373: Data Structures and Algorithms (Spring 2001)

• CSE 599: Alternative Computing Paradigms (Winter 2001)

Prior Work:

• Learning in Mobile Robots:
  • Short Video of the Robot
  • Papers:
    • Rao, R.P.N. and Fuentes, O. (1998) "Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory," Autonomous Robots Vol. 5, pp. 297-316, and Machine Learning, Vol. 31, pp. 87-113.
    • Learning Navigational Behaviors using a Predictive Sparse Distributed Memory (The Fourth Int. Conf. on Simulation of Adaptive Behavior (SAB) 1996)
    • Hierarchical Learning of Reactive Behaviors (IEEE Int. Conf. on Systems, Man, & Cybernetics 1995)
    • Perceptual Homing using Self-Organizing Networks (Proc. of World Congress on Neural Networks 1995)
• Learning Spatiotemporal Receptive Fields from Natural Images (Tech Report 97.4,Dept of Comp Sci, Univ of Rochester, 1997)
• Dynamic Appearance-Based Recognition (Proceedings of CVPR'97, pp. 540-546, 1997)
• Eye Movements in Visual Cognition (Technical Report, 1997)
• Robust Kalman Filters (Technical Report, 1997)
• Kalman Filter Models for Invariant Recognition, Motion, and Stereo (Technical Report, 1996)
• Object-Centered Neglect (Computational Neuroscience: Trends in Research 1997)
• Modeling Human Eye Movements in Visual Search (Advances in NIPS 8, pp. 830-836, 1996)
• Face Recognition using Natural Basis Functions (Proceedings of IJCAI*95, pp. 10-17, 1995)
• An Active Vision Architecture based on Iconic Representations (Artificial Intelligence 78, pp. 461-505, 1995)
• Learning Saccadic Eye Movements using Multiscale Spatial Filters (Advances in NIPS 7, pp. 893-900, 1995)

Other Activities:

• Band member, The Smashing Notkins (2002-2003).
  Official Band Photo (copyright: Melissa Garcia, 2003).