Other courses of interest to those in neuroscienceNote: this list is entered by users and so is not "official"
Course descriptions not shown below or on linking webpages can be found here
Also, visit the schedule of courses at cornell.
COGST 201: Cognitive Science in Context Laboratory (Also PSYCH 201)
Instructor(s): David J. Field (Spr. 2005) | | A laboratory course that explores the theories of cognitive science and provides direct experience with the techniques of cognitive science, in relation to the full range of both present and anticipated future activities in the workplace, the classroom, and in everyday life. Discussions of laboratory exercise results, supplementation of laboratory topics, and analyses of challenging primary research literature are done in meetings of the entire class. Laboratory exercises, which are done on an individual or small group basis, include both pre-planned investigations and student-developed experiments. Use of digital computers as well as the Internet, e-mail, and web sites are integral components of the course. |
| | BIONB 221: Neurobiology and Behavior I: Introduction to Behavior | | A general introduction to the field of behavior. Topics include evolution and behavior, behavioral ecology, sociobiology, chemical ecology, communication, orientation and navigation, and hormonal mechanisms of behavior. |
| BIONB 222: Introduction to Neurobiology
Instructor(s): Ben J. Arthur (Spr. 2007) | BIOAP 311: Introductory Animal Physiology
Instructor(s): Ellis R. Loew (Fall 2005) | | This is a general course in animal physiology concentrating on regulation and regulatory pathways. |
| HD 319: Memory & The Law (Also HD 619)
Instructor(s): Charles J Brainerd (Fall 2007) | PSYCH 342: Human Perception: Applications to computer graphics, art and visual display
Instructor(s): David J. Field (Fall 2004) | | Our present technology allows us to transmit and display information through a variety of media. To make the most of these media channels, it is important to consider the limitations and abilities of the human observer. The course considers a number of applied aspects of human perception with an emphasis on the display of visual information. Topics covered include: "three-dimensional" display systems, color theory, spatial and temporal limitations of the visual systems, attempts at subliminal communication, and "visual" effects in film and television. |
| MATH 362: Dynamic Models in Biology (Also BIOEE 362)
Instructor(s): John Guckenheimer (Spr. 2004) | | Introductory survey of the development, computer implementation, and applications of dynamic models in biology and ecology. Case-study format, covering a broad range of current application areas such as regulatory networks, neurobiology, cardiology, infectious disease management, and conservation of endangered species. Students also learn how to construct and study biological systems models on the computer using a scripting and graphics environment. |
| | M&AE 463: Neuromuscular Biomechanics (Also BMEP 463) | | Modeling and simulation of biomechanical systems using mechanics, dynamics, and control principles. Physiology of neurons and muscles introduced and related to the production of force and movement in biological systems. Representation of neuromuscular systems as simultaneous equations. Exploration of the muscular redundancy problem using optimization methods and general-purpose languages (such as Mathematica or MATLAB). Selected clinical applications. |
| | PSYCH 465: Topics in High-Level Vision (Also COGST 465, COM S 392) | | High-level vision is a field of study concerned with functions such as visual object recognition and categorization, scene understanding, and reasoning about visual structure. It is an essentially cross-disciplinary endeavor, drawing on concepts and methods from neuroanatomy and neurophysiology, cognitive psychology, applied mathematics, computer science, and philosophy. The course concentrates on a critical examination of a collection of research publications, linked by a common thread, from the diverse perspectives offered by the different disciplines. Students write biweekly commentaries on the assigned papers and a term paper integrating the material covered in class. |
| | ECE 547: Computer Vision | | Computer acquisition and analysis of image data with emphasis on techniques for robot vision. This course concentrates on descriptions of objects at three levels of abstraction: segmented images (images organized into subimages that are likely to correspond to interesting objects), geometric structures (quantitative models of image and world structures), and relational structures (complex symbolic descriptions of images and world structures). The programming of several computer-vision algorithms is required. |
| | TAM 578 : Nonlinear Dynamics and Chaos | | Introduction to nonlinear dynamics, with applications to physics, engineering, biology, and chemistry. Emphasizes analytical methods, concrete examples, and geometric thinking. Topics: one-dimensional systems; bifurcations; phase plane; nonlinear oscillators; and Lorenz equations, chaos, strange attractors, fractals, iterated mappings, period doubling, renormalization. |
| | VETMM 610: Cellular and Molecular Pharmacology | | This graduate-level course surveys the molecular and cellular aspects of receptor mechanisms, signaling pathways, and effector systems. Topics include drug-receptor interactions; ligand- and voltage-gated ion channels; G protein pathways; growth factor signaling; lipid signaling; calcium; nutrient and nitric oxide signaling; and mechanisms of receptor-mediated effects on neural excitability, electrical pacemakers, muscle contraction, and gene expression. |
| | COM S 478 : Machine Learning | | Learning and classifying are two of our basic abilities. Machine learning is concerned with the question of how to train computers to learn from experience, to adapt and make decisions accordingly. This course introduces the set of techniques and algorithms that constitute machine learning as of today, including inductive inference of decision trees, the parametric-based Bayesian learning approach, Bayesian belief networks and Hidden Markov Models, non-parametric methods, discriminent functions and support vector machines, neural networks, stochastic methods such as genetic algorithms, unsupervised learning and clustering, and other issues in the theory of machine learning. These techniques are used today to automate procedures that were previously performed by humans as well as to explore untouched domains of science. |
| | COM S 627 : Computational Biology: The Machine Learning Approach | | This is a graduate-level course in computational biology that focuses on machine learning models and their application to computational problems in biology. Some topics covered are supervised (Support Vector Machines, Hidden Markov Models, deterministic and probabilistic suffix trees) and unsupervised (embedding, PCA, ICA, clustering) learning in computational biology, advanced statistical analysis of sequences, analysis of microarrays, and modeling of complex systems (Bayesian Belief Networks, DEA). |
| | COM S 678 : Advanced Topics in Machine Learning | | This course extends and complements COM S 478 and COM S 578, giving in-depth coverage of new and advanced methods in machine learning. In particular, we connect to open research questions in machine learning, giving starting points for future work. The content of the course reflects an equal balance between learning theory and practical machine learning, making an emphasis on approaches with practical relevance. Topics include support vector machines, clustering, Bayes nets, boosting, model selection, learning orderings, and inductive transfer. |
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Please report corrections, questions, comments, and problems to:
Lori Miller (lmm8 AT cornell.edu)
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