Courses

• CAS CL 519: History of the Greek Language
  Presents a comprehensive historical approach to the Greek language, tracing and elucidating major changes with attention to structure, grammar, syntax, vocabulary, and elements of style. Cannot be taken for credit in addition to CAS CL 319.
• 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 546: Early Christian Latin Literature
  Introduction to the reading and interpretation of important works of early Christian literature (3rd -- 6th centuries) in Latin, for students of classics (esp. Latin), theology, and related historical disciplines. The focus is on language, literary form, relation to 'classical' literature, and historical contexts. Topics vary. Also offered as CAS CL 346.
• 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 CL 596: Early Christian Greek Literature
  Introduction to the reading and interpretation of important works of early Christian literature (1st --6th centuries) in Greek, for students of classics (esp. Greek), theology, and related historical disciplines. The focus is on language, literary form, relation to 'classical' literature, and historical contexts. Topics vary. Also offered as CAS CL 396.
• 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 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 492: Directed Study
  
• 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 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.
• CAS CS 101: Introduction to Computers
  The computer is presented as a tool that can assist in solving a broad spectrum of problems. This course provides a general introduction designed to dispel the mystery surrounding computers and introduces the fundamental ideas of programs and algorithms. (Does not count for CS major or minor credit.) Carries MCS divisional credit in CAS.
• CAS CS 103: Introduction to Internet Technologies and Web Programming
  Introduction to the basic architecture and protocols underlying the operation of the Internet with an emphasis on Web design, Web application programming, and algorithmic thinking. General familiarity with the Internet is assumed. (Does not count for CS major credit.) Carries MCS divisional credit in CAS.

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