CNS 780: SPECIAL TOPICS IN COMPUTATIONAL NEUROSCIENCE
Dept. of Cognitive and Neural Systems
Boston University
SPRING 2011
Tues. 1-4 PM
677 Beacon Street, Basement Auditorium

Course Syllabus and Required Readings

Professor Eric Schwartz
Office: Room 310, 677 Beacon Street
Office hours: W 1-5
Phone: 353-6179
e-mail: eric@bu.edu

OVERVIEW: Computational neuroscience refers to the area of overlap between computer science, mathematics,engineering and neuroscience. On the one hand, methods of computer graphics, image processing, and numerical methods have provided basic tools for application in neuroscience. At the most basic level, the visualization and reconstruction of neuroanatomical material obtained from histology (serial sections) and tomographic studies (PETT, MRI, etc.) are dependent on relatively sophisticated computational techniques. At a higher level, models of the neuronal, columnar, and topographic brain structures of interest to experimental neuroscientists are being modeled, and increasingly, can only be understood in terms of, sophisticated computational models. Finally, at the functional level, the methods by which the nervous system achieves its powerful computational abilities are of increasing interest to engineers and computer scientists, who seek to learn from the brain, as well as neuroscientists and psychologists, who seek to learn about the brain.

These three levels of computational neuroscience will be explored in the context of a reading seminar for advanced graduate students in the Dept. of Cognitive and Neural Systems, the College of Engineering, and the School of Medicine. The goal of the course is exposure to the literature of computational neuroscience. Original papers in the areas of the structure, function and modeling of primate visual cortex will provide one major area of coverage. Special emphasis will be placed on the columnar and topographic structure of visual cortex, and the computational and functional models that have been developed in the context of visual cortex. A second major area of emphasis will be on technological applications in the areas of active (computer) vision.

This material is by nature multidisciplinary: students will be expected to have a strong background in at least one of neuroscience, computer science or computer technology. A mathematical background which includes a working knowledge of applied linear algebra, fourier analysis, and advanced calculus will be assumed. However, some attempt will be made to provide a self-contained presentation: a brief review of all advanced material will be provided.

REQUIREMENTS: All students must complete either a term paper or a computer project. Topics for these will be suggested during the seminar. There will be a mid-term exam, but the term paper assignment will be used instead of a final exam.

Problem sets, or brief literature reviews, will be assigned during class. The final grade will be weighted equally between the mid-term, the problem sets, and the term paper/project assignment.

E-MAIL An alias cn780 will be set up and used to broadcast information to students enrolled in cn780.

CLASS WEBSITE is at CN780 WIKI. There is a login and password which will be distributed during the first class meeting.

This web site is interactive (its a "wiki"), so two-way interchanges can occur between all of us. In addition, the syllabus, all important announcements, and electronic versions of the readings will be posted there, when available, and also additional information. Please consult the wiki each week for updates to the syllabus, readings, or other matters.

Reading Material Copies of all required reading will be placed on the wiki in electronic form. Supplementary material will be available, by e-mail appointment, at my office at 677 Beacon Street. Students will be responsible for reading material listed as required on the class wiki. Students are expected to have completed the required reading listed for a given week by the date of the lecture.

1 Introduction and Review

The first lecture will be a preview of the course, in order to allow a decision about the interest and appropriateness of background for each student, and a review of some basic material in pattern recognition and image processing.

PRE-REQUISITES

Advanced calculus: Newtons Method, Constrained optimization, Stokes Theorem,Differential forms
Linear algebra: Eigenvalue, Inverse, Singular Value Decomposition
Neuroscience Neurons, Maps, Columns, Features, etc.
Programming: Unix, C, C++,Matlab or Mathematica, Python or Perl, Image Processing and Graphics (OpenGL)

Associative memory, classification and clustering
Machine Vision and Active Vision

1.4.2 Control

1.4.3 Neuronal pattern formation

Maps
Columns
Neuronal morphology

1.4.4 Computer aided neuroanatomy

Tomography: CAT, PETT, MRI
Tissue subsections

1.4.5 Data analysis and interpretation

EEG and Evoked Potentials
Optical dye and 2DG, Cytochrome oxidase
Single and multiple unit recording

1.4.6 VLSI

Analog VLSI (Mead, Mahowald, Synaptics,Inc.)
Digital VLSI (CISC, RISC, DSP, FPGA, ASIC, MOSIS)

1.4.7 Applications

Time series
Pattern recognition
Machine vision, video, active vision
Expert systems
Robotics and control
Communications, GSTN, ISDN

4 Topography: Experimental

5 Models of Topography and Psychophysical Scaling

6 Columns and maps: models of computational function

7 Spatial Representation: Neurons as feature extractors

11 Olfaction: Sensation and spatial mapping

[Turin, 2006]

Apr 27,2010 TERM PAPER PRESENTATION

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Download Postscript.

1 Introduction and Review

2 Cortical Columns: experimental observation

3 Columns:Modeling

6 Columns and maps: models of computational function

7 Spatial Representation: Neurons as feature extractors

8 Spatial Frequency, Scale, and Multi-resolution

9 Non Linear Diffusion

10 Computer aided neuroanatomy of visual cortex

11 Olfaction: Sensation and spatial mapping