 |
Spring 1998 Courses |
| This page last updated 1 January 1998 | |
|
Spring 1998 Courses |
| This page last updated 1 January 1998 | |
This course will provide an overview of parallel-distributed processing models of aspects of perception, memory, language, knowledge representation, and learning. The course will consist of lec tures describing the theory behind the models as well as their implementation, and students will get hands-on experience running existing simulation models on workstations. Prerequisites: 85-211 Cognitive Psychology, extensive experience using computers, and 21-122Calculus 2 or permission of the instructor.
This course will review what has been learned of the neural bases of cognition through studies of brain-damaged patients as well as newer techniques such as brain stimulation mapping, regional metabolic and blood flow imaging, and attempt to relate these clinical and physiological data to theori es of the mind cast in information-processing terms. The course will be organized into units corresponding to the traditionally-defined subfields of cognitive psychology such as perception, memory and language. In each area, we will ask: To what extent do the neurological phenomena make contact with the available cognitive theories? When they do, what are their implications for these theories (i.e., Can we confirm or disconfirm particular cognitive theories using neurological data?)? When they do not, what does this tell us about the parses of the mind imposed by the theories and methodologies of cognitive psychology and neuropsychology? Prerequisites: 85-211, Cognitive Psychology and either 85-310, Research Methods in Cognitive Psychology, or 85-350, Rese arch Methods in Cognitive Neuroscience.
This course surveys classical artificial neural network architectures such as perceptrons, the LMS algorithm, backpropagation, Hopfield networks, Boltzman machines, competitive learning, and self-organizing feature maps. Students will experiment with various neural network simulations written in Matlab, and also write simulations of their own. The course also covers basic pattern recognition concepts, connectionist models of cognitive phenomena, and a variety of neural network applications such as optical character recognition, speech recognition, and robot control.EVALUATION: there will be several programming assignments in Matlab, plus a midterm and final exam. Prior knowledge of Matlab is not required.
For more details, see: http://www.cs.cmu.edu/afs/cs/project/connect/intro-course/index.html.
This course will provide a graduate-level introduction to Artifical Intelligence tailored towards the algorithms and applications of robotics, manufacturing, and engineering disciplines. It has three main themes, detailed below: (i) Knowledge Representation, (ii) Search and (iii) Inference & Learning. Several areas of AI which are usually taught in CS and psychology departments have been curtailed for this audience: in particular Natural Language processing is reduced to an overview giving the current capabilities of the field. Cognitive aspects of AI have also been reduced. Finally, perception, which is taught in detail in other Robotics core courses, is not duplicated here. In place of these reductions, the course focusses strongly on modern numerical approaches to AI and robotics. These include bayes nets, classical decision-theoretic problems such as scheduling, optimal and learning control of markov systems. This course also covers in detail: motion planning and spatial reasoning in addition to other subjects on the border of AI and robotics, including neural nets, qualitative reasoning and fuzzy logic.
An introduction to mammalian physiology presented in the context of control systems with emphasis on functional interactions within and between systems. Physiological systems to be covered include neuroanatomy; electrophysiology; neurophysiology; muscile physiology; sensory physiology; and learning and cortical physiology.
This is a hands-on laboratory course covering the principle of bioelectical recording. Students study living nerve and muscle tissue maintained in life-support systems, learn to use electric stimulators and electrodes, and are taught to record the action, contraction, and resting membrane potentials from the muscles and nerves. The course also teaches parallel, non-invasive studies on human electrophysiology, such as EEG brain potentials and EMG muscle potentials. We use minicomputers and general-purpose computers for averaging bioelectrical data in time and frequency domain.
This course will survey topics found in the vast expanses of the science of neurobiology. Neurobiology is the study of the nervous system, its development, its function and its diseases. Aspects of the developmentand cell biology of the nervous system from the formation of the nervous system in the embryo to the demise of the brain as in Alzheimer's diseases will be discussed. The physiology and molecular genetics of the function of a neuron and the nervous system will also be discussed with the aim of understanding current and classical topics of neurobiology from a modern point of view.
This course offers an introduction to modeling methods in neuroscience. It illustrates how models can extend and evaluate neuroscience concepts. Basic techniques of modeling biophysics, excitable membranes, small network and large scale net work systems will be introduced. The course begins with a consideration of mathematical models of excitable membranes, including the Hodgkin- Huxley model and simplifications such as the Morris-Lecar and FitzHugh-Nagumo models. It will provide hands-on laboratory experience in modeling membranes, neurons, and neural networks. The course explores the use of differential equations, numerical simulation and graphical techniques for modeling neural systems. The range of topics include simulations of electrical properties of membrane channels, single cells, neuronal networks and cognitive simulation. Students will be afforded laboratory experience in computer modeling, and they will develop computational neuroscience models in the course. Prerequisites for the course include basic knowledge of calculus, neuroscience, and some computer programming.
Study of attention behavioral results, nuerophysiology and computational models. Syllabus available upon request.
This course will provide an introduction to cognitive neuroscience, a field of study in which an interdisciplinary approach is used to investigate the relationship of the mind and the brain. While the field of cognitive neuroscience has emerged relatively recently (within the past 10 years), the growth of new societies and journals devoted to this field are just a few indicators of its vitality. The course will complement existing lower-level courses in Biopsychology, Sensation & Perception, and Human Learning & Memory in two respects. First, it will build upon the base of knowledge students are exposed to in these core courses by specifically emphasizing the connection between congition and nuerobiology. Secondly, the course will require students to integrate their training in psychology with information and methodologies drawn from other disciplines (e.g., computer science, neuroscience, neurology). As much as learning the "facts" of cognitive neuroscience, students will be expected to think critically about the unique ways in which different disciplines and methodologies can enhance our understanding of the mind-brain relationship.
This undergraduate course may be of interest to CNBC students whose primary area of expertise is not neuroscience, but who want to learn more about synaptic transmission. The course will examine the mechanisms by which neurotransmitters are synthsized and released and the biochemistry of synaptic responses. Basic physiological, biochemical, and morphological charateristics of neuronal transmission will be discussed. An emphasis will be placed on the experimental approaches used to examine these processes.
This is the second in the introductory graduate series of courses designed to provide an overview of neuroscience. Much of the course is devoted to the biochemical basis of chemical synaptic transmission, with an emphasis on regulatory mechanisms. Prerequisites: None. However, students who have not had Neuroscience 1 first should obtain consent of the instructor to enroll.
This course will discuss the neural control of movement in detail, including planning of movement in the cortex, relay of motor commands to the brainstem and spinal cord, coordination of movement by the cerebellum, adjustment of movement via brainstem and spinal cord reflexes, and execution of movement through contraction of muscle fibers. Attention will be given to both basic science and clinical issues in motor control. Course format will include both lectures and discussion of original research papers. This course is open to both advanced undergraduate students and graduate students who desire a detailed background concerning control of movement, and will be useful for premedical students, physical therapy students, students with interests in robotics, and students whose research deals with issues of movement control.
This course is a component of the introductory graduate sequence designed to provide an overview of neuroscience. The course will provide an introduction to topographic anatomy of the mammalian nervous system and to the functional organization of sensory systems, motor systems, regulatory systems, and systems involved in higher brain functions. This course is taught in a lecture/lab format. A background in basic biology is required. If students have not had college biology courses, they should obtain consent of the instructor to enroll.
This course is designed to provide a survey of some of the major neurological and psychiatric disorders for the non-clinician. Each session will focus on a particular disorder and will include a patient presentation (live or by videotape), and a discussion of the etiology, epidemiology, pathophysiology, and treatment of that disorder.
A practical applied course, covering experimental design and statistical analysis useful for research in Neuroscience. A computer component is an integral part of the course. Emphasis is on independent groups ANOVA both simple and complex, simple repeated measures ANOVA, multiple comparisons, testing assumptions, and power analysis. If time permits, other topics such as regression analysis and complex repeated measures ANOVA will be considered. Prerequisites: An undergraduate statistics course or permission of instructor.
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