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Neuroscience Institute  ›  Academics  ›  Undergraduate Training  ›  Minor in Neural Computation

Minor in Neural Computation

Neural computation is a scientific enterprise to understand the neural basis of intelligent behaviors from a computational perspective. Study of neural computation includes, among others, decoding neural activities using statistical and machine learning techniques, and developing computational theories and neural models of perception, cognition, motor control, decision-making and learning. The neural computation minor allows students to learn about the brain from multiple perspectives, and to acquire the necessary background for graduate study in neural computation. Students enrolled in the minor will be exposed to, and hopefully participate in, the research effort in neural computation and computational neuroscience at Carnegie Mellon University.

The Minor in Neural Computation is an inter-college minor jointly sponsored by the Dietrich College of Humanities and Social Sciences, the School of Computer Science, and the Mellon College of Science and is coordinated by the Neuroscience Institute.

Goal and Eligibility

The neural computation minor is open to students in any major of any college at Carnegie Mellon. It seeks to attract undergraduate students from computer science, psychology, engineering, biology, statistics, physics, and mathematics from DC, CIT, MCS, and SCS. The primary objective of the minor is to encourage students in biology and psychology to take computer science, engineering and mathematics courses on the one hand, and to encourage students in computer science, engineering, statistics and physics to take courses in neuroscience and psychology on the other, and to bring students from different disciplines together to form a community. The curriculum and course requirements are designed to maximize the participation of students from diverse academic disciplines. The program seeks to produce students with both basic computational skills and knowledge in cognitive science and neuroscience that are central to computational neuroscience.

Application

Students must apply for admission no later than November 30 of their senior years; an admission decision will usually be made within one month. Students are encouraged to apply as early as possible in their undergraduate careers so that the director of the Neural Computation minor can provide advice on their curriculum, but should contact the program director any time even after the deadline.

To apply, send email to the director of the Neural Computation minor Dr. Tai Sing Lee (tai@cnbc.cmu.edu) and copy Melissa Stupka (mstupka@andrew.cmu.edu). Include in your email:
  • Full name
  • Andrew ID
  • Preferred email address (if different)
  • Your class and College/School at Carnegie Mellon
  • Semester you intend to graduate
  • All (currently) declared majors and minors
  • Statement of purpose (maximum 1 page) – Describes why you want to take this minor and how it fits into your career goals
  • Proposed schedule of required courses for the Minor (this is your plan, NOT a commitment)
  • Research projects you might be interested in

Curriculum

The Minor in Neural Computation will require a total of five courses: four courses drawn from the four core areas (A: neural computation, B: neuroscience, C: cognitive psychology, D: intelligent system analysis), one from each area, and one additional depth elective chosen from one of the core areas that is outside the student’s major. The depth elective can be replaced by a one-year research project in computational neuroscience. No more than two courses can be double counted toward the student’s major or other minors. However, courses taken for general education requirements of the student’s degree are not considered to be double counted. A course taken to satisfy one core area cannot be used to satisfy the course requirement for another core area. The following listing presents a set of current possible courses in each area. Substitution is possible but requires approval.

A. Neural Computation:

  • 15-386 Neural Computation (9 units)
  • 15-883 Computational models of neural systems (12 units)
  • 85-419 Introduction to parallel distributed processing (9 units)
  • 86-375/15-387 Computational Perception (9 units)
  • Pitt MATH 1800 Introduction to mathematical neuroscience (9 units)

B. Neuroscience

  •  03-362 Cellular neuroscience (9 units)
  • 03-363 Systems neuroscience (9 units)
  • 85-765 Cognitive neuroscience (9 units)
  • Pitt NROSCI 1000 Introduction to neuroscience (9 units)
  • 18-690/42-630 Introduction to Neuroscience for Engineers (12 units)

C. Cognitive Psychology

  • 85-211 Cognitive psychology (9 units)
  • 85-213 Human information processing and artificial intelligence (9 units)
  • 85-412 Cognitive modeling (9 units)
  • 85-419 Introduction to parallel distributed processing (9 units)
  • 85-426 Learning in humans and machines (9 units)
  • 85-765 Cognitive neuroscience (9 units)

D. Intelligent System Analysis

  • 10-601 Machine learning (9 units)
  • 15-381 Artificial intelligence (9 units)
  • 15-386 Neural Computation (9 units)
  • 15-486 Artificial neural networks (9 units)
  • 15-494 Cognitive robotics (9 units)
  • 16-299 Introduction to feedback control systems (9 units)
  • 16-311 Introduction to Robotics (9 units)
  • 16-385 Computer vision (9 units)
  • 18-290 Signals and systems (9 units)
  • 24-352 Dynamic systems and control (9 units)
  • 36-225 Introduction to probability and statistics (9 units)
  • 36-247 Statistics for laboratory sciences (9 units)
  • 36-401 Regression (9 units)
  • 36-410 Introduction to Probability Models (9 units)
  • 36-746 Statistical methods for neuroscience (9 units)
  • 42-632 Neural Signal Processing (12 units)
  • 86-375/15-387 Computational Perception (9 units)
  • 86-631/42-631 Neural Data Analysis (9 units)

Prerequisites

The required courses in the above four core areas require a number of basic prerequisites including basic programming skills at the level of 15-110 (introductory/intermediate programming) and basic mathematical skills at the level of 21-122 (Integration, differential equations and approximation) or their equivalents. Area B Biology courses require, at minimum, 03-121 (Modern Biology). Students might skip the prerequisites if they have the permission of the instructor to take the required courses.

Prerequisite courses are typically taken to satisfy the students’ major or other requirements. In the event that these basic skill courses are not part of the prerequisite or required courses of a student’s major, one of them can potentially count toward the five required courses (e.g. the depth elective), conditional on approval.

Research Requirements (Optional)

The minor itself does not require a research project. The student however may replace the depth elective with a year-long research project. In special circumstances, a research project can also be used to replace one of the five courses, as long as (1) the project is not required by the student’s major or other minor, (2) the student has taken a course in each of the four core areas (not necessarily for the purpose of satisfying this minor’s requirements), and (3) has taken at least three courses in this curriculum not counted toward the student’s major or other minors. Students interested in participating in the research project should contact any faculty engaged in computational neuroscience or neural computation research at Carnegie Mellon or in the University of Pittsburgh. A useful webpage that provides listing of faculty in neural computation and computational neuroscience is http://www.cnbc.cmu.edu/cnbc-directory/ . The director of the Minor program will be happy to discuss with students about their research interest and direct them to the appropriate faculty.

Fellowship Opportunities

Year Long Undergraduate Fellowship in Neural Computation

The Neuroscience Institute currently provides a yearlong fellowship in computational neuroscience to Carnegie Mellon undergraduate students to carry out mentored research in neural computation. The fellowship has course requirements similar to the requirements of the minor. Students do not apply to the fellowship program directly. They have to be nominated by the faculty members who are willing to mentor them. Therefore, students interested in the full-year fellowship program should contact and discuss research opportunities with any NI training faculty at Carnegie Mellon or University of Pittsburgh working in the area of computational neuroscience and ask for their nomination.

Summer Undergraduate Research Program in Neural Computation

Undergraduates interested in receiving research training in computational neuroscience are encouraged to apply to an NIH-sponsored summer program at the Neuroscience Institute. Starting in late May or early June each year, a select group of talented undergraduates will embark on a 10-week residential program that provides intensive, mentored research experiences in computational and theoretical neuroscience.

Administrative Contacts

  • Director: Dr. Tai Sing Lee (tai@cnbc.cmu.edu)
  • Administrative coordinator: Melissa Stupka (mstupka@cnbc.cmu.edu)
  • Administrative consult: Catharine Fichtner (cathyf@cs.cmu.edu)
  • Liaison and undergraduate advisers in the different colleges for additional advice:
  • SCS: Dr. Tom Cortina (tcortina@andrew.cmu.edu)
  • MCS: Dr. Beck Campanaro (bcampana@andrew.cmu.edu)
  • CIT: Dr. Kurt Larsen (klarsen@cmu.edu)
  • DC: Dr. Erik Thiessen (thiessen@andrew.cmu.edu)