The Center for the Neural Basis of Cognition

Current CNBC Course Offerings
This page last updated 05 December 2001
You can also skip ahead to Spring 2002 offerings, or view past classes from Spring 2001, Fall 2000, Fall 1999, Fall 1998, Spring 1998, or Fall 1997.

Fall 2001

First day of classes for CMU & Pitt: Monday, August 27, 2001.

CMU Computer Science

15-681 Machine Learning Fall: 12 units (undergraduate version)

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to spot high-risk medical patients, recognize human faces, detect credit card fraud, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as datamining, decision tree learning, neural network learning, statistical learning methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, minimum description length principle, and Occam's Razor. Short programming assignments include hands-on experiments with various learning algorithms. Typical assignments include neural network learning for face recognition, and decision tree learning from databases of credit records. Prerequisite: 15-212, or permission of the instructor.

 

15-781 Machine Learning: 12 units (graduate version)

Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practice of machine learning from a variety of perspectives. We cover topics such as learning decision trees, neural network learning, statistical learning methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Occam's Razor. Programming assignments include hands-on experiments with various learning algorithms. Typical assignments include neural network learning for face recognition, and decision tree learning from databases of credit records.

CMU Mechanical Engineering

24-779 Section A, Special Topics in Controls: Human Systems and Control: 9 or 12 units

This course covers the mechanisms of human motor systems and control, using arm movements as an example. The course starts with the anatomy of muscles, sensors, spinal cords, and brains; then functional analyses of these system components will follow. After system analysis, all components are integrated to study feedback control dynamics. Using physiological studies such as psychophysical and lesion experiments, this course covers classical and modern theories of how the nervous system may control movements. Advanced topics include adaptation, representation, coordinate systems, cognitive involvement, and rehabilitation techniques for motor-impaired patients. A project / presentation is required to take the course for 12 units. Prerequisites: 21-241, 21-260, 24-451, or permission of the instructor.

CMU Robotics

16-720 Section A, Computer Vision: 12 units

Description: This course deals with the science and engineering of computer vision, that is, the analysis of patterns in visual images of the world with the goal of reconstructing and understanding the objects and processes in the world that are producing them. The emphasis is on physical, mathematical, and information processing aspects of vision.

Topics covered include image formation and representation, camera geometry and calibration, multi-scale analysis, segmentation, contour and region analysis, energy-based techniques, reconstruction of based on stereo, shading and motion, 3-D surface representation and projection, and analysis and recognition of objects and scenes using statistical and model-based techniques. The material is based on a recent graduate-level textbook augmented with research papers, as appropriate. The course involves considerable Matlab programming exercises.

CMU Psychology

85-713 Human Information Processing and Artificial Intelligence: 12 units

This class will review various results in cognitive psychology (attention, perception, memory, problem solving, language) and use of artificial intelligence techniques to simulate cognitive processes. The prerequisites for this course are 15-211 and 85-211. A 3 unit course is taught along with 85-213 which will teach LISP.

 

85-717 Cognitive Modeling and Intelligent Tutoring Systems: 9 units

This course will focus on the combination of cognitive psychology and artificial intelligence required to develop intelligent computer-assisted instruction. A background in artificial intelligence (minimally LISP) and cognitive psychology is required. Half of the course will be project-oriented. We will learn the production system GRAPES and work up to producing an expert system and a tutor for a fragment of calculus.

 

85-721 Language and Thought: 9 units

This course allows the student to explore ways in which the mind shapes language and language shapes the mind. Why are humans the only species with a full linguistic system? Some of the questions to be explored are: What kinds of mental abilities allow the child to learn language? What are the cognitive abilities needed to support the production and comprehension of sentences in real time? How do these abilities differ between people? Are there universal limits on the ways in which languages differ? Where do these limitations come from cognition in general or the specific language facility? Why is it so hard to learn a second language? Are there important links between language change and cultural change that point to links between language and culture? Prerequisite: either 85-211 Cognitive Psychology, 80-180 The Nature of Language, or equivalent background.

 

85-728 Neuro Basis of Cognitive Development: 9 units

 

85-855 Introduction to Cognitive Neuroscience: 9 units

An introductory-level course for undergraduates and graduate students with no prior background in cognitive neuroscience. Note: this is not a substitute for 85-765, the cognitive neuroscience core course for CNBC students.

 CMU Statistics

36-743 Statistical Methods for the Behavioral and Social Sciences: 12 units

Pitt Mathematics
 

NONE.

Pitt Neuroscience

NROSCI/MSNBIO 2100 Cellular and Molecular Neurobiology: CR HRS: 7.0 [CNBC core course]
NROSCI/MSNBIO 2101 Cellular and Molecular Neurobiology Conference: CR HRS: 2.0

This is a required course for students in the Program in Neuroscience, and also satisfies the CNBC core requirement in neurophysiology. The course is very demanding. For this reason, CNBC students not in the Neuroscience program might prefer to take Jon Johnson's undergraduate neurophysiology course in the Spring instead. The regular syllabus will be augmented with half a dozen supplementary lectures specifically for CNBC graduate students.

 The Cellular and Molecular course covers protein structure and function, gene expression, neuronal development, membrane properties, the action potential, synaptic transmission, and second messenger systems, and synaptic plasticity. It consists of a lecture section and a conference section; students should sign up for both. The conference section is devoted to discussion of papers in the primary literature.

NROSCI  2011 Functional Neuroanatomy: CR HRS: 4.0

This course covers the basic structure of the central nervous system from spinal cord to cerebral cortex.  The major sensory, motor and integrative neural systems of the human brain are discussed.  Based on an understanding of normal neural connections and brain function, the anatomical and physiological basis of various neurological disorders of the nervous system will be explored.

Special Enrollment Counseling is required for registration.  Students should contact Dr. Sesack for permission to register.

 

NROSCI  3023 Seminar in Cognitive Neuroscience: CR HRS: 1.0 to 3.0

This is a journal club devoted to cognitive neuroscience.

NROSCI  3024 Seminar in the Hippocampus: CR HRS: 1.0 to 3.0

This is a journal club devoted to the hippocampus.

Pitt Psychology

PSY   2005 Statistical Analysis I / Advanced Statistics-UG: CR HRS: 3.0

 

PSY   2205 Psychopathology: CR HRS: 3.0

The aim of this course is to provide a critical background in research strategies, phenomena, empirical research, and models of adult psychopathology. The course emphasis will be on etiological and pathological research, with both psychological and biological findings to be discussed. Course concentration will be on the major psychopathologies with clinical onset in adulthood, including schizophrenia, affective disorders, anxiety, addictions, and eating disorders. Conceptual and methodological issues that cross diagnostic categories will be stressed. Treatment approaches and differential diagnosis will be covered but not emphasized.

 

PSY   2400 Human Cognition: Research Methods: CR HRS: 2.0

This course covers experimental research methods including proposing an experiment, literature search, experimental design, programming experiments, simple data analysis, research writing, and oral presentation. Specific research techniques for reaction time, memory and learning methods, protocol analysis, and human subject methods are covered. The course provides students experience with the technical skills for advanced research. The course will require a number of laboratory assignments, execute human experiment, write-up an experiment and an in-class final.


Spring 2002

First day of classes: Pitt January 7 2002; CMU January 14 2002.

CMU Computer Science

15-783/85-791 Computational Perception and Scene Analysis: 12 units

This course teaches advanced aspects of perception, scene analysis, and recognition in both the visual and auditory modalities, concentrating on those aspects that allow us and animals to behave in natural, complex environments. The goal of this course is to teach how to reason scientifically about problems and issues in perception and scene analysis, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms.

Specific topics include sensory coding, perceptual invariance, spatial vision and sound localization, visual and auditory scene segmentation, many aspects of attention, and the basics of recognition in natural visual and auditory scenes. Mathematical topics covered include Bayesian inference, information theory, linear systems analysis, neural networks, independent component analysis, and various algorithms in computational vision and audition.

CMU Psychology

85-719 Introduction to Parallel Distributed Processing: 9 units [CNBC Core Course]

This course provides an overview of parallel distributed processing (PDP) models of aspects of perception, memory, language, knowledge representation, and learning. The course consists of lectures describing the mathematical and computational theory behind artificial neural network models as well as their implementation. Students also acquire substantial hands-on experience manipulating existing simulation models on computer workstations, and they are expected to complete term projects involving novel simulation work. Prerequisites include course 85-211 (Cognitive Psychology), extensive experience using computers, and course 21-122 (Calculus 2) or permission of the instructor.

 

85-765 Cognitive Neuroscience: 9 units  [CNBC core course]
Cross-listed as Pitt Neuroscience NEUSC 2005.

This course will cover fundamental findings and approaches in cognitive neuroscience, with the goal of providing an overview of the field at an advanced level. Topics will include high-level vision, spatial cognition, working memory, long-term memory, learning, language, executive control, and emotion. Each topic will be approached from a variety of methodological directions, for example, computational modeling, cognitive assessment in brain-damaged humans, non-invasive brain monitoring in humans, and single-neuron recording in animals. Lectures will alternate with sessions in seminar format. Prerequisite: Permission of Instructor (Graduate standing or undergraduates must have strong prior background in at least one relevant discipline, and permission of instructor.

 

85-770 Perception: 9 Units

Perception, broadly defined, is the construction of a representation of the external world, for purposes of thinking about it and acting in it. Although we often think of perception as the processing of inputs to the sense organs, the world conveyed by the senses is ambiguous, and cognitive and sensory systems interact to interpret it. In this course, we will examine the sensory-level mechanisms involved in perception by various sensory modalities, including vision, audition, and touch. We will learn how sensory coding interacts with top-down processing based on context and prior knowledge and how perception changes with learning and development. The goals include not only imparting basic knowledge about perception, but fostering an appreciation for the beauty of perceptual systems and providing some new insights into everyday experiences.

CMU Statistics

TBD

Pitt Psychology

TBD

Pitt Neuroscience

NROSCI 2012 Neurophysiology: CR HRS: 3.0 [CNBC Core Course]

This is an undergraduate neurophysiology course that will be augmented with some additional lectures for CNBC graduate students who are not neuroscientists and are not able to meet the time demands of NROSCI 2100/2101. For those students, the course satisfies the CNBC's neurophysiology core requirement; they should sign up for NROSCI 2012 (the graduate version) rather than 1012. Students in the Program in Neuroscience must take Cellular and Molecular Neurobiology (NROSCI 2100/2101, offered in the fall) instead of this course.

NROSCI/MSNBIO 2102 Systems Neurobiology: CR HRS: 6.0 [CNBC Core Course]

This course incorporates neuroanatomical and neurophysiological approaches to examine the integrative functions of the brain. It consists of lectures and neuroanatomy laboratories focusing on structure/function relations using human brain specimens. The course covers in detail the major sensory, motor and behavioral regulatory systems of the brain. The course satisfies the CNBC core requirement in neuroanatomy.

Pitt Mathematics

MATH 3925 Stochastic Differential Equations: CR HRS: 3.0

This course will introduce students to a number of techniques that are useful in analysis of noisy systems. Probability concepts such as moments, cumulants, multivariate distributions and the central limit theorem will be introduced. Various random processes such as the Poisson processes are described along with spectral methods and the notion of correlation. A discussion of Markov processes leads to the analysis of one-step processes. The Master equation and random walks are described. Next, the Fokker-Planck equation will be introduced. The derivation and solutions for particular cases are given. First passage time problems amd multi-dimensional FP equations are also covered. The Langevin equation, linear stochastic equations, and other stochastic differential equations are discussed. Additional topics may be covered depending on the time and student needs.

Prerequisites: Math 0220, 0230, 0280. Recitations: none. Expected class size: 25 students. This course is offered on an irregular basis.

Top of Page \ Training \ CNBC Home Page \ Page Credits \ Comments \ Search