Declaring a concentration in Computational Neuroscience requires some homework on your part and a bit of paperwork by all of us. 

1. Acquaint yourself with the course requirements of the concentration and with other relevant courses offered at Brown that might form part of your program. You can also discuss the concentration at open houses in the Department of Neuroscience and with department representatives at various informational sessions held during the year or reach out to the Neuroscience Education Program Manager with questions or to set up a pre-advisor meeting.
2. Lay out a schedule, semester by semester, of the courses you will need to complete the concentration during your remaining years at Brown. Make sure that courses taken in the same semester are given at different times, paying close attention to laboratories. Make sure that you have arranged to complete the prerequisites for the courses you schedule. You may use the Course Plan Worksheet as a guide.
3. Please complete the declaration in ASK and then email neuroundergrad@brown.edu to be assigned a concentration advisor. You and your new advisor will be sent an email as confirmation; in addition, you will be pre-assigned to your advisor on the ASK (Advising Sidekick) online concentration declaration system.
4. Make an appointment to see this advisor to discuss your plans and preferences.
5. After consulting with your advisor, you can update your concentration declaration on ASK, which will automatically be sent to your concentration advisor for approval.
6. Changes in your concentration courses can be made but this should never be done without first consulting your concentration advisor. You are responsible for the consequences of any changes made without departmental approval.

The following courses are required for a Computational Neuroscience concentration:

Background Courses

• MATH0100
• APMA0350
• MATH0520 or MATH0540 or APMA0260
• Statistics: APMA1650 or AMPA1655 or CLPS0900 or BIOL0495 or CSCI1450

Core Courses

• NEUR0010
• NEUR1020 or NEUR1030
• CSCI0150 (or equivalent) and CSCI0200
• NEUR0680
• Senior Seminar in Computational Neuroscience (currently in development)

Computational Neuroscience

Two courses from the following list:

• CLPS1492: Computational Cognitive Neuroscience
• NEUR1440: Mechanisms and Meaning of Neural Dynamics
• NEUR1660: Neural Computation in Learning and Decision Making
• CLPS1291: Computational Methods for Mind, Brain, and Behavior
• CLPS1810: Computational Molecular Biology
• NEUR1940B: Deep Learning in Neuroethology
• NEUR1630: Open-Source Big Data Neuroscience Lab
• CLPS1950: Deep Learning for Brains, Minds, and Machines
• CLPS1850: Language Processing in Humans and Machines
• NEUR2110: Statistical Neuroscience

Artificial Intelligence

One course from the following list:

• CSCI0410: Foundations of AI
• CSCI1410: Artificial Intelligence
• CSCI1411: Foundations of AI
• CSCI1420: Machine Learning
• CSCI1430: Computer Vision
• CSCI1460: Computational Linguistics
• CSCI1470: Deep Learning
• DATA2060: Machine Learning: from Theory to Algorithms
• HIST1956S: History of Artificial Intelligence

Upper-Level Neuroscience

Two courses that will enhance your understanding of the field of neuroscience. While electives need not be from the neuroscience department, the following list are common courses taught by Neuroscience and other departments that are often used as electives. We encourage students to explore the broader course catalog and consult with their concentration advisor to explore the full range of electives, rather than limiting themselves to this list: *These electives must be of 1000-level or above.

• CLPS1400: The Neural Bases of Cognition
• CLPS1920: Cognitive Neuropsychology
• ENGL1900Z: Neuroaesthetics
• ENGN1220: Neuroengineering
• NEUR1540: Neurobiology of Learning and Memory
• NEUR1650: Structure of the Nervous System
• NEUR1740: The Diseased Brain

Ethics

One course from the following list:

• CSCI1805: Computers, Freedom, and Privacy
• CSCI1951I: CS for Social Change
• CSCI1951Z: Fairness in Automated Decision Making
• DATA0080: Data, Ethics, and Society
• ENGN1800: Social Impact of Emerging Technologies: The Role of Scientists and Engineers
• PHIL0401: Ethics of Digital Technology
• PHIL0403: Ethics and Politics of Data
• APMA1910: Race and Gender in the Scientific Community
• STS1700T: Race, Gender, and Technology in Everyday Life
• ECON1000: Using Big Data to Solve Economic and Social Problems

Electives

Two courses that will enhance your understanding of the field of computational neuroscience. These electives are not limited to a specific department, and are able to be any of the courses already listed for this concentration (though, you cannot cross-count an elective with a named requirement). The following list are courses that we recommend be used as electives, however, we encourage students to explore the broader course catalog and consult with their concentration advisor to explore the full range of electives, rather than limiting themselves to this list. You can also substitute TWO semesters of independent study (NEUR1970 or equivalent course from another department) for one elective course.

• APMA0160: Introduction to Scientific Computing
• APMA0200: Introduction to Modeling
• APMA0360: Partial Differential Equations
• APMA1070: Quantitative Models of Biological Systems
• APMA1170: Introduction to Computational Linear Algebra
• APMA1360: Applied Dynamical Systems
• APMA1660: Statistical Inference II
• APMA1690: Computational Probability and Statistics
• APMA1710: Information Theory
• APMA1740: Recent Applications of Probability and Statistics
• APMA1860: Graphs and Networks
• APMA1941D: Pattern Theory
• BIOL1435: Computational Methods for Studying Demographic History with Molecular Data
• BIOL1555: Methods in Informatics and Data Science for Health
• CLPS0450: Brain Damage and the Mind
• CLPS0800: Language and the Mind
• CSCI0535: Linear Algebra for Machine Learning
• CSCI1010: Theory of Computation
• CSCI1570: Design and Analysis of Algorithms
• CSCI1951A: Data Science
• CSCI1951Q: Algorithmic Aspects of Machine Learning
• ENGN2912P: Topics in Optimization
• MATH1610: Probability
• PHYS1610: Biological Physics