Laboratories Terry Sejnowski: Computational Neurobiology Laboratory The long range goal of CNL is to understand the computational resources of brains from the biophysical to the systems levels. The central issues being addre- ssed are how dendrites integrate synaptics ignals in neurons, how networks of neurons generate dynamical patterns of activity, how sensory information is represented in the cerebral cortex, how memory representations are formed and consolidated during sleep, and how visuo-motor transformations are adaptively organized. New techniques have been developed for modeling cell signaling using Monte Carlo methods (MCell) and the blind separation of brain imaging data into functionally independent components (ICA). Te-Won Lee: Lee Laboratory The goals of our laboratory is to develop new algorithms and methods for signal processing, image processing and generic data analysis. We are highly interested in understanding how the brain represents its sensory Information and we believe that research in neural information processing systems can provide novel solutions. Our projects include novel methods for Robust speech recognition in noisy environments, Learned image coding and compression, New pattern recognition methods for biomedical data classification. Javier Movellan & Marian S. Bartlett: Machine Perception Laboratory The goals of the MPL are: 1. To develop computer systems that recognize and react to natural speech commands,facial expressions, gestures and     body motions. 2. To help analyze how the brain works by building computer systems that face perceptual problems similar to those faced by the brain. 3. To help understand the brain and the mind by analyzing the statistical structure of natural audio-visual signals. Scott Makeig: Swartz Center for Computational Neuroscience The Swartz Center for Computational Neuroscience brings together senior and junior scientists from the computational, physical, physiological and cognitive sciences to develop and exploit new functional brain imaging methods and computational tools with a goal of understanding and interpreting the functional significance of brain activity supporting human cognition and behavior. The goal of the Center is to observe and model how functional activities in multiple brain areas interact dynamically to support human cognition and interaction. Howard Poizner: Poizner Lab Our goal is to better understand the neural bases of human motor control.  Our approach is to analyze the nature of the breakdown in motor control in patients with selective failure of specific motor (or sensory) systems of the brain, such as occurs in Parkinson's disease, cerebellar ataxia, or limb deafferentation. Toward this end, we have developed novel methods of imaging and graphic analysis of spatiotemporal patterns inherent in digital records of movement trajectories. One domain of our studies is Parkinson's disease. One hypothesis that we have been evaluating is that a key function of the basal ganglia, which is then impaired in Parkinson’s disease, is support for the recoding and integration of proprioceptive signals with other sensory and motor signals in order to enable accurate movements. We are investigating the haptic sensitivity of Parkinson’s patients; we are investigating how Parkinson’s patients reach to targets presented in 3D space under various conditions of visual feedback; and using 3D immersive virtual environments, we are investigating how Parkinson’s patients learn to adapt their movements in altered sensorimotor environments. Peter Rowat: Dynamics of Motor Behavior Laboratory The DMBL goals are: 1. Understand how small biological CPGs produce rhythmic motor behavior that is also highly variable and adaptive. 2. Analyze and understand the contributions of noise and unstable (hence chaotic) dynamics to the variability observed in all parts of the nervous system but particularly in small CPGs. 3. Build small hardware robotic mechanisms to illustrate and augment our understanding of CPG dynamics.