Courses

• CAS CL 513: Studies in Classical Civilization
  Topic for Spring 2007: Theory of Literature. Literary theories represent a continuing dialogue, through which we make sense of everything we read. This course debates a series of classic issues - from dramatic realism to poetic language - in the light of theory, and literature, from ancient Greece to the present day. Also offered as CAS EN 490 and CAS LL 550.
• CAS CL 520: Studies in Latin Literature
  Theory and practice of Latin letter writing, focusing on the letters of Cicero and Pliny the Younger. Special attention to the letters' literary and rhetorical aspects. Comparisons to verse epistles of Ovid, philosophical letters of Seneca, and samples of the medieval ars dictaminis.
• CAS CL 521: Survey of Latin Literature I
  Historical survey from archaic Latin through Republican literature; introduction to classical scholarship. For advanced students wishing to increase their language skills through extensive reading.
• CAS CL 522: Survey of Latin Literature II
  Survey of Latin authors focusing on the period of the early Empire; introduction to classical scholarship. For advanced students wishing to increase their language skills through extensive reading.
• CAS CL 530: Latin Prose Composition
  Practice in set and free composition of Latin prose, aimed at developing advanced language proficiency.
• CAS CL 540: Studies in Roman History
  Topic for Spring 2007: TBA.
• CAS CL 560: Studies in Ancient Greek History
  
• CAS CL 561: Survey of Greek Literature 1
  Reading course designed to study the history of Greek literature through a chronological survey of representative authors and genres: Homer through the archaic age.
• CAS CL 562: Survey of Greek Literature 2
  Reading course designed to study the history of Greek literature through a chronological survey of representative authors and genres: Classical through Hellenistic period.
• CAS CL 563: Greek Prose Composition
  Close study of exemplary Greek prose as the basis for original composition in Greek of sentences and short passages, then more extensive prose pieces.
• CAS CN 210: Introduction to Computational Models of Brain and Behavior
  Introduction to important concepts in cognitive neuroscience and computational modeling of biological neural systems. Combines a systems-level overview of brain function with an introduction to modeling of brain and behavior using neural networks. Also offered as CAS NE 204.
• CAS CN 330: Introduction to Computational Models of Vision
  Explores the psychological, biological, mathematical, and computational foundations of visual perception. Mathematically specified neural and computational models elucidate the structure and dynamics of the mammalian visual system. Also offered as CAS NE 330.
• CAS CN 340: Introduction to Computational Models of Skilled Decision and Action
  Humans are facile skill learners. What brain circuits and learning mechanisms enable skilled selection of plans and enactment of the plans chosen? Integrates interdisciplinary efforts to understand brain functions well enough to allow accurate computer simulations of skill learning. Also offered as CAS NE 340.
• CAS CN 350: Introduction to Computational Models of Learning
  Introduction to computational models of how the brain learns and remembers, and how human and model systems access memories during recognition and recall. Cognitive, neural, and machine learning models; mathematical and computational methods; brain science; history, applications, and large-scale simulations. Also offered as CAS NE 350.
• CAS CN 360: Introduction to Computational Neuroscience of Speech, Language, and Hearing
  Introduces the foundations of auditory perception including the mammalian auditory pathway, speech and language perception, links with speech production, auditory scene analysis, and music perception from a computational perspective. Laboratory computer assignments elucidate functional properties of these systems. Also offered as CAS NE 360.
• 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.

Note that this information may change at any time.

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