Cognitive & Neural Systems

• CAS CN 500: Computational Methods in Cognitive and Neural Systems
  Introduction to mathematical methods and computer simulation for modeling cognitive and neural systems. Topics include computer simulation methods, control theory, difference and differential equations, digital signal processing, image processing, optimization, and statistics. Readings from current literature emphasize theory and applications relevant to the study of cognitive and neural systems.
• CAS CN 510: Principles and Methods of Cognitive and Neural Modeling I
  Explores psychological, biological, mathematical, and computational foundations of behavioral and brain modeling. Topics include organizational principles, mechanisms, local circuits, network architectures, cooperative and competitive non-linear feedback systems, associative learning systems, and self-organizing code-compression systems. The adaptive resonance theory model unifies many course themes. CAS CN 510 and 520 may be taken concurrently.
• CAS CN 520: Principles and Methods of Cognitive and Neural Modeling II
  Analyzes three main traditions in models of learning: unsupervised (self-organized) learning, supervised learning (learning with a teacher), and reinforcement learning. Architectures studied include adaptive filters, back propagation, competitive learning, self-organizing feature maps, gradient descent procedures, Boltzmann machines, simulated annealing, neocognitron, and gated dipoles. CAS CN 510 and 520 may be taken concurrently.
• CAS CN 530: Neural and Computational Models of Vision
  Current models of mammalian visual processes are constrained by experimental and theoretical results from psychology, physiology, computer science, and mathematics. The course evaluates the explanatory adequacy of competing neural and computational models of such processes as edge detection, textural grouping, shape-from-shading, stereopsis, motion detection, and color perception. Students perform computer simulations of some of the examined models.
• CAS CN 540: Neural and Computational Models of Adaptive Movement Planning and Control
  Neural models of eye, arm, hand, orofacial, and leg movements are presented and compared to reveal general organizational principles and specialized neural circuit designs for motor learning and performance. Issues include trajectory formation, synchronization of synergists, variable velocity control, adaptive gain control, map formation, load compensation, serial order, and inflow versus outflow as sources of sensory-motor information.
• CAS CN 550: Neural and Computational Models of Recognition, Memory, and Attention
  Develops neural network models of how internal representations of sensory events and cognitive hypotheses are learned and remembered, and how such internal representations enable recognition and recall of these events to occur. Various neural pattern recognition models are analyzed. Special emphasis is placed on stable self-organization of pattern recognition and recall codes in unpredictable and noisy environments, notably by adaptive resonance theory models, and on how such codes direct attention toward predictively relevant combinations of features, while attenuating irrelevant background cues. Experimental data and theoretical predictions from cognitive psychology, neuropsychology, and neurophysiology of normal and abnormal individuals are analyzed.
• CAS CN 560: Neural and Computational Models of Speech Perception and Production
  Develops neural network models of speech perception and production processes. Emphasis is placed on the role of learning and on the specialized neural designs that have evolved for purposes of speech communication. Practical, including industrial, applications of neural networks for speech processing are also reviewed.
• CAS CN 570: Neural and Computational Models of Conditioning, Reinforcement, Motivation, and Rhythm
  Develops neural and computational models of how humans and animals learn to successfully predict environmental events and generate behavioral actions that satisfy internally defined criteria of success or failure. Reinforcement learning and its homeostatic (drive, arousal, rhythm) and nonhomeostatic (reinforcement) modulators are analyzed in depth. Recognition learning and recall learning networks are joined to the reinforcement learning network to analyze how these several processes cooperate to generate successful goal-oriented behavior. Maladaptive behaviors and certain mental disorders are analyzed from a unified theoretical perspective. Applications to the design of freely moving adaptive robots are noted.
• CAS CN 580: Introduction to Computational Neuroscience
  This introductory level course focuses on building a background in neuroscience, but with emphasis on computational approaches. Topics include basic biophysics of ion channels, Hodgkin-Huxley theory, use of stimulators such as NEURON and GENESIS, recent applications of the compartmental modeling technique, and a survey of neuronal architectures of the retina, cerebellum, basal ganglia, and neocortex.
• 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 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 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 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.
• GRS CN 901: Dir Study Cns
  
• GRS CN 902: Dir Stdy in Cns
  
• GRS CN 911: Dr Patrn Models
  
• GRS CN 915: Dr Visn Models
  
• GRS CN 921: Dr Spch Models
  
• GRS CN 925: Dr Motor Models
  
• GRS CN 931: Dr Cond Models
  

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