Cognitive & Neural Systems

  • CAS CN 510: Principles and Methods of Cognitive and Neural Modeling I
    Undergraduate Prerequisites: CASMA226 (or equivalent; can be taken in parallel); and CASCS108 or CASCS111 or ENGEK127 (or equivalent); and CASNE101 (or equivalent; can be taken in parallel); or consent of instructor.
    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
    Undergraduate Prerequisites: one semester of linear algebra and consent of instructor.
    Graduate Prerequisites: one semester of linear algebra and consent of instructor.
    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 540: Neural and Computational Models of Adaptive Movement Planning and Control
    Undergraduate Prerequisites: CAS CN 510; or consent of instructor.
    Graduate Prerequisites: CAS CN 510; or consent of instructor.
    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 560: Neural and Computational Models of Speech Perception and Production
    Undergraduate Prerequisites: CAS CN 510; or consent of instructor.
    Graduate Prerequisites: CAS CN 510; or consent of instructor.
    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
    Undergraduate Prerequisites: CAS CN 510; or consent of instructor.
    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.
  • GRS CN 730: Models of Visual Perception
    Graduate Prerequisites: CAS CN 530; and consent of instructor.
    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 780: Topics in Computational Neuroscience
    Undergraduate Prerequisites: CAS MA 225 and CAS MA 242; or consent of instructor.
    Graduate Prerequisites: CAS MA 225 and CAS MA 242; or consent of instructor.
    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.