The Center for the Neural Basis of Cognition

Spring 2001 Courses
Spring 2001

CMU Computer Science

15-883 Computational Models of Neural Systems: 12 units [CNBC Core Course]

This course offers an in-depth look at biological neural systems from a computational perspective. We will examine a variety of brain structures whose anatomy and physiology are sufficiently well understood that it's possible to theorize about the representations and algorithms they employ. There will be some neuroscience tutorial lectures for those with no prior background in this area. Students will also have the opportunity to experiment with some actual computational models running in Matlab.

 

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 course emphasizes both the experimental approaches of scientific disciplines and the computational approaches of engineering disciplines.

Each topic in the course begins by studying the ethology of natural behaviors, analyzing and decomposing these to identify the essential components that are required for the total behavior in a natural environment. This aspect of the course follows the lines of scientific reasoning and key experimental results that lead to our current understanding of the important computational problems in perception and scene analysis. The course then surveys the most important solutions to these problems, focusing on the idealizations and simplifications that are used to achieve practical 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.

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.  

87-770 Perception: 9 Units

This is a basic course in sensation and perception. It covers some neurophysiology of sensory systems, particulary vision; basic perceptual topics like pattern recognition; and some cognitive topics like spatial attention and top-down processing. There is some coverage of audition (speed and space perception), olfaction, taste, and touch.

CMU Statistics

36-746 Quantitative Methods in Neuroscience: 12 units

This course provides a brief survey of statistical methods that are of use in cognitive neuroscience.  Topics may include probability, confidence intervals and significance tests, maximum likelihood, Bayesian analysis, information theory, ROC curves, Poisson processes and the peristimulus time histogram; Fourier analysis and signal processing; principal components analysis and independent components analysis; and the bootstrap, regression, and nonparametric regression.

Pitt Psychology

PSY 2575 Affective Cognitive Neuroscience: CR HRS 3.0

The seminar will explore the relationship between affective and cognitive processes. Background lectures will introduce broad areas of study, but most of the course will be focused on readings drawn from the psychological, psychiatric, and neuroscientific literatures. Potential topics of discussion include the relationship between emotion, memory, and stress; the role of emotional and motivational processing on rational decision-making; and potential mechanisms that underlie susceptibility to and recovery from mood disorders.

Pitt Neuroscience

NROSCI  2102/2103 Systems Neurobiology: CR HRS: 4.0 + 2.0 [CNBC Core Course]

This course covers the anatomy of the mammalian nervous system, and systems-level theories of neural function. It consists of a lecture section (NROSCI 2102) and a "conference" section (NROSCI 2103); CNBC students should sign up for both. The course satisfies the CNBC core requirement in neuroanatomy.

Pitt Mathematics

MATH 3375, PSY 2480: Computational Neuroscience Methods [CNBC Core Course]

Sampling of topics covered: neuron spiking; firing rates and spike statistics; reverse correlations and receptive fields; neural decoding (discrimination, population decoding, spike train decoding); electrical properties of neurons; single-compartment models; modeling channels and synpatic conductances; the Hodgkin-Huxley model; the cable equation; conductances and morphology; levels of modeling; network models (firint rate models, feedforward and recurrent networks, excitatory-inhibitory networks, stochasic networks); plasticity and learning; supervised and unsupervised learning.

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