Mike Lewicki Associate Professor, Computer Science and the CNBC Carnegie Mellon University Phone: (412) 268-3921 Fax: (412) 268-5060 Email: lewicki@cnbc.cmu.edu Individual Website: http://www.cs.cmu.edu/~lewicki

Ph.D., California Institute of Technology Research Interests My work investigates the computational principles underlying how the brain represents and learns higher level representations of natural patterns. To understand how the brain accomplishes such tasks as speech or object recognition, we must not only tackle the question of how the brain encodes sensory information, but also how this code is processed to represent more abstract properties of the stimulus. For example, what are the basic computations underlying the formation of perceptual invariances such as the ability to recognize words independent of speaker or objects independent of orientation? Research in my lab works toward these goals by developing and applying principles of information representation and processing. Part of my work focuses on the issue of sensory coding: How should natural patterns be encoded? One approach that my research has developed is the application of probabilistic models to learn codes that are efficient in an information theoretic sense. Given a particular sensory environment, this top-down approach makes predictions about the properties of neural codes at the population level. When this approach is applied to natural images, the resulting representation very closely matches the receptive field properties of visual cortex cells. This framework also demonstrates that the model system encodes natural images more efficiently than many common Fourier or wavelet-based coding schemes. Because the theory based on general probabilistic models of a high dimensional data space, it can be applied to a variety of sensory patterns including static and dynamic visual images as well as patterns from different acoustic environments. A second aim of my work is to develop algorithms for learning hierarchical representations. These are important because they can extract successively more abstract properties of the sensory patterns and may provide insight into how the brain carries out more complex forms of information processing. One result of this work has been a computational explanation for the role of feedback, which is ubiquitous in the brain, but poorly understood. The long term goal of this research is to develop abstract neural architectures and learning algorithms that help elucidate the details of the higher-level processes that allow the brain to perceive and process natural stimuli under a wide range of conditions. Recent Publications Lee, T.-W. and Lewicki, M. S. (2001) Image processing using ICA mixture models. In S. Roberts and R. Everson, editors, Independent Component Analysis: Principles and Practice, pages 234-253. Cambridge University Press, Cambridge, MA. Lee, T-W. and Lewicki, M. S. (2002) Unsupervised classification, segmentation and enhancement of images using ICA mixture models. IEEE Trans. Image Proc., 11(3):270-279. Lewicki, M. S. (2002) Efficient coding of natural sounds. Nature Neuroscience, 5(4):356-363. Lewicki, M. S. (2002) Efficient coding of time-varying patterns using a spiking population code. In R. P. N. Rao, B. A. Olshausen, and M. S. Lewicki, editors, Probabilistic Models of the Brain: Perception and Neural Function, pages 223-234. MIT Press, Cambridge, MA. Karklin, Y. and Lewicki, M. S. (2003) Learning higher-order structures in natural images. Network: Computation in Neural Systems, 14:483-499. Lewicki, M.S., and Sejnowski, T.J. (2000). Learning overcomplete Lewicki, M.S., and Sejnowski, T.J. (2000). Learning overcomplete representations. Neural Computation 12(2):337-365. Lewicki, M.S. and Olshausen, B.A. (1999). A Probabilistic Framework for the Adaptation and Comparison of Image Codes. J. Opt. Soc. Am. A: Optics, Image Science, and Vision 16(7): 1587-1601. Lewicki, M. S. (1998) A review of methods for spike sorting: the detection and classification of neural action potentials. Network: Computation in Neural Systems, 9(4):R53-R78, 1998. Lewicki, M.S. and Sejnowski, T.J. (1997). Bayesian unsupervised learning of higher order structure. (eds.) Mozer, Jordan, and Petsche. Advances in Neural and Information Processing Systems, 9: 529-535.