Computational Neuroscience

This is an archived copy of the 2012-13 catalog. To access the most recent version of the catalog, please visit http://catalogs.uchicago.edu.

Catalog Home › The College › Interdisciplinary Opportunities › Computational Neuroscience

Contacts

Undergraduate Primary Contact

Faculty Adviser Nicholas G. Hatsopoulos
Anatomy Building, Room 202
773.702.5594
Email

Administrative Contact

Administrative Director Nicole Kaminski-Ozturk
J233, SBRI
773.302.6371
Email

Website

http://bscd.bsd.uchicago.edu/content/minor-programs

Computational neuroscience is a relatively new interdisciplinary area of inquiry that is concerned with how components of animal and human nervous systems interact to produce behaviors. It relies on quantitative and modeling approaches to understand the function of the nervous system and to design human-made devices that duplicate behaviors. Course work in computational neuroscience can prepare students for graduate studies in neurobiology or psychology, in the mathematical or engineering sciences, or in areas of medicine such as neurology or psychiatry. It can lead either to traditional academic careers or to opportunities in the corporate world.

An undergraduate degree in computational neuroscience is not available at the University of Chicago, but a minor in computational neuroscience is offered by the Biological Sciences Collegiate Division. This minor is a good option for students who are majoring in biological sciences and are interested in mathematical approaches to biology; or for students who are majoring in computer science, mathematics, physics, psychology, or statistics and are interested in neuroscience. For details, see the Biological Sciences section elsewhere in this catalog.

Students electing this minor must have completed, or placed out of, the equivalent of a year of collegiate-level calculus and must have completed the general education requirement for the biological sciences.

Summary of Requirements for the Minor in Computational Neuroscience

BIOS 24231	Methods in Computational Neuroscience	100
BIOS 24232	Computational Approaches to Cognitive Neuroscience	100
BIOS 26210-26211	Mathematical Methods for Biological Sciences I-II	200
BIOS 29408	Signal Analysis and Modeling for Neuroscientists	100
Total Units		500

Instead of completing a formal minor, students can easily fashion an organized course of study in computational neuroscience by selecting appropriate general education courses and electives.

For updated information on computational neuroscience activities and undergraduate programs, visit cns.bsd.uchicago.edu .

Suggested General Education Courses

Students majoring in biological sciences can elect either the BIOS 20180s or the BIOS 20190s sequence.

One of the following sequences:		200
MATH 15100-15200	Calculus I-II
MATH 16100-16200	Honors Calculus I-II
SOSC 14100-14200-14300	Mind I-II-III	300
Suggested Electives
BIOS 24203	Introduction to Neuroscience	100
BIOS 24204	Cellular Neurobiology	100
BIOS 24205	Systems Neuroscience	100
BIOS 24208	Survey of Systems Neuroscience	100
BIOS 24246	Neurobiology of Disease I	100
BIOS 24247	Neurobiology of Disease II	100
PSYC 20300	Biological Psychology	100
PSYC 20400	Cognitive Psychology	100
PSYC 20700	Sensation and Perception	100

Faculty

Faculty associated with this interdisciplinary area participate in a three-quarter sequence in computational neuroscience, teach upper-level courses relevant to computational neuroscience, and participate in an ongoing computational neuroscience seminar series.

Computational Neuroscience Courses

BIOS 24231. Methods in Computational Neuroscience. 100 Units.

Topics include (but are not limited to): Hodgkin-Huxley equations, Cable theory, Single neuron models, Information theory, Signal Detection theory, Reverse correlation, Relating neural responses to behavior, and Rate vs. temporal codes.

Instructor(s): S. Bensmaia, L. Osborne, J. Maclean, D. Freedman Terms Offered: Winter. L.
Prerequisite(s): BIOS 26210 and BIOS 26211 which must be taken concurrently, or consent of instructor.
Equivalent Course(s): CPNS 34231

BIOS 24232. Computational Approaches to Cognitive Neuroscience. 100 Units.

This course is concerned with the relationship of the nervous system to higher order behaviors (e.g., perception, object recognition, action, attention, learning, memory, and decision making). Psychophysical, functional imaging, and electrophysiological methods are introduced. Mathematical and statistical methods (e.g. neural networks and algorithms for studying neural encoding in individual neurons and decoding in populations of neurons) are discussed. Weekly lab sections allow students to program cognitive neuroscientific experiments and simulations.

Instructor(s): N. Hatsopoulos Terms Offered: Spring
Prerequisite(s): BIOS 26210, a course in systems neuroscience, and knowlege using Matlab, or consent of instructor.
Equivalent Course(s): CPNS 33200, ORGB 34650, PSYC 34410,CPNS

BIOS 24246. Neurobiology of Disease I. 100 Units.

This seminar course is devoted to basic clinical and pathological features and pathogenic mechanisms of neurological diseases. The first semester is devoted to a broad set of disorders ranging from developmental to acquired disorders of the central and peripheral nervous system. Weekly seminars are given by experts in the clinical and scientific aspects of the disease under discussion. For each lecture, students are given a brief description of clinical and pathological features of a given set of neurological diseases followed by a more detailed description of the current status of knowledge of several of the prototypic pathogenic mechanisms.

Instructor(s): C. Gomez, Staff Terms Offered: Winter
Prerequisite(s): NURB 31800 or BIOS 24203
Equivalent Course(s): CPNS 34600,NURB 34600,CCTS 40100

BIOS 26210-26211-26212. Mathematical Methods for Biological Sciences I-II-III.

BIOS 26210. Mathematical Methods for Biological Sciences I. 100 Units.

This course focuses on ordinary differential equations as models for biological processes changing with time. The emphasis is on dynamical systems theory, stability analysis, and different phase portraits, including limit cycles and chaos. Linear algebra concepts are introduced and developed. Numerous biological models are analyzed, and labs introduce numerical methods in MATLAB.

Instructor(s): D. Kondrashov Terms Offered: Autumn
Prerequisite(s): BIOS 20151 or BIOS 20152
Equivalent Course(s): CPNS 31000,ISTP 26210,PSYC 36210

BIOS 26211. Mathematical Methods for Biological Sciences II. 100 Units.

This course continues the study of time-dependent biological processes and introduces discrete-time systems, studying period-doubling, and onset of chaos. Fourier transform methods are used to analyze temporal and spatial variation, leading to the study of partial differential equations. The diffusion, convection, and reaction-diffusion equations are all used to model biological systems. Finally, common optimization methods are introduced. In labs, computational techniques are used to analyze sample data and study models.

Instructor(s): D. Kondrashov Terms Offered: Winter
Prerequisite(s): MATH 15300 or equivalent
Equivalent Course(s): CPNS 31100,ISTP 26211,PSYC 36211

BIOS 26212. Mathematical Methods for Biological Sciences III,Mathematical Models for Biological Sciences III. 100 Units.

This course covers basic mathematical probability, probability distributions, correlation, principal and independent component analysis, and stochastic processes. Stochastic behavior is ubiquitous at all levels of biology, and examples range from electrophysiology to bioinformatics. In labs, students use stochastic models to model and analyze these systems. ,For course description contact BIOS.

Instructor(s): D. Kondrashov, Terms Offered: Spring,
Prerequisite(s): MATH 15300 or equivalent,
Equivalent Course(s): CPNS 31200,ISTP 26212,CPNS 31200,ISTP 26212,PSYC 36212

BIOS 29408. Signal Analysis and Modeling for Neuroscientists. 100 Units.

The course provides an introduction into signal analysis and modeling for neuroscientists. We cover linear and nonlinear techniques and model both single neurons and neuronal networks. The goal is to provide students with the mathematical background to understand the literature in this field, the principles of analysis and simulation software, and allow them to construct their own tools. Several of the 90-minute lectures include demonstrations and/or exercises in Matlab.

Instructor(s): W. van Drongelen Terms Offered: Spring
Prerequisite(s): BIOS 26210 and 26211, or consent of instructor.
Note(s): This course meets requirements for the biological sciences major only for students specializing in neuroscience.
Equivalent Course(s): CPNS 32110

2012-13 College Catalog