Cognitive & Neural Systems

• GRS CN 699: Teaching College Cognitive and Neural Systems I
  The goals, contents, and methods of instruction in cognitive and neural systems. General teaching-learning issues. Required of all teaching fellows.
• GRS CN 700: Computational and Mathematical Methods in Neural Modeling
  Introduction to advanced computational topics used in quantitative modeling. Techniques from signal processing, probability, statistics, vector quantization, optimal control, and ordinary and partial differential equations. Theory, simulations, and techniques illustrated with neural networks and other behavioral and biological models.
• GRS CN 710: Advanced Topics in Neural Modeling
  Examines current neural network models to prepare students to participate in research on an advanced level. Topics are chosen based upon the latest discoveries and methodologies in the field and upon the research interests of advanced CNS students.
• GRS CN 720: Neural and Computational Models of Planning and Temporal Structure in Behavior.
  Identifies characteristics and principles of serial plan formulation, choice, and learning in humans. Includes theoretical analyses and neural network modeling of such processes as they appear in communicative speech and gesture, handwriting, typing tool use, and object assembly.
• GRS CN 730: Models of Visual Perception
  Offers advanced survey of topics in the neural and computational modeling of psychophysical data in mammalian vision. Assignments include oral presentations on selected readings and a term paper containing a literature review and model development and analysis.
• GRS CN 740: Topics in Sensory Motor Control
  Topics include spatial representation, speech production, and rhythmic movement. Representations appropriate for handwriting, reaching, speaking, and walking are investigated with emphasis on different levels of representation and interactions between these levels. Material includes psychophysical data, neurophysiology, and neural models.
• GRS CN 760: Topics in Speech Perception and Recognition
  This course surveys advanced topics in automatic speech recognition and auditory representation of speech signals, especially as they relate to speech perception. The course is constructed around a thorough introduction to state-of-the-art techniques in automatic speech recognition and relates these to perspectives obtained from perceptual and neurophysiological research. The course begins with the necessary fundamentals in digital signal processing and statistical pattern recognition, then discusses the major techniques in automatic speech recognition, including neural networks, hidden markov models, and dynamic programming. It explores the relation of these techniques to neurophysiological processing and psycholinguistic data, and evaluates neural models of auditory processing and speech perception. Modeling techniques, including parameter optimization and goodness-of-fit tests, are covered.
• GRS CN 780: Topics in Computational Neuroscience
  In this seminar, recent research papers and applications in computational neuroscience are reviewed. Topics covered include cortical modeling, analog VLSI, active perception, robotic control, stereo vision, and computer-aided neuroanatomy.
• GRS CN 810: Topics in Cognitive and Neural Systems
  Topic for Fall 2011: Adaptive computing: From virtual to robotic agents. Students design biologically- inspired computational models that implement autonomous perception, decision making, and action in virtual and robotic agents. A term project, executed by small groups, is required, including a problem statement and an implementation of a behavioral task.

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