This multidisciplinary concentration spans many fields, including computer science, neuroscience, cognitive science, applied math, and data science. Students studying Computational Neuroscience will learn to use computational models of the brain and nervous system to study complex biological processes and overcome the limitations of human experimentation. They will also learn to use the brain and nervous system as a model to improve the power and efficiency of artificial systems. Concentrators will think critically about the impact of their work on society and understand how biases can negatively influence computational models.
Standard program for the Sc.B. Degree
| Background Courses (must take one of each): | ||
| Calculus | ||
| Single Variable Calculus, Part II | ||
| Differential Equations | ||
| Applied Ordinary Differential Equations | ||
| Linear Algebra | ||
| Linear Algebra | ||
or MATH 0540 | Linear Algebra With Theory | |
| Statistics | ||
| Statistical Inference I | ||
or APMA 1655 | Honors Statistical Inference I | |
or CLPS 0900 | Statistical Methods | |
or BIOL 0495 | Statistical Analysis of Biological Data | |
or CSCI 1450 | Advanced Introduction to Probability for Computing and Data Science | |
| Core Concentration Courses: | ||
| NEUR 0010 | The Brain: An Introduction to Neuroscience | 1 |
| NEUR 1020 | Principles of Neurobiology | 1 |
| or NEUR 1030 | Neural Systems | |
| CSCI 0111 | Computing Foundations: Data | 1 |
| or CSCI 0112 | Computing Foundations: Program Organization | |
| or CSCI 0150 | Introduction to Object-Oriented Programming and Computer Science | |
| or CSCI 0170 | Computer Science: An Integrated Introduction | |
| or CSCI 0190 | Accelerated Introduction to Computer Science | |
| CSCI 0200 | Program Design with Data Structures and Algorithms | 1 |
| or CSCI 0190 | Accelerated Introduction to Computer Science | |
| NEUR 0680 | Introduction to Computational Neuroscience | 1 |
| Two Computational Neuroscience Electives From The Below List: | 2 | |
| Computational Cognitive Neuroscience | ||
| Mechanisms and Meaning of Neural Dynamics | ||
| Neural Computation in Learning and Decision-Making | ||
| Computational Methods for Mind, Brain and Behavior | ||
| Computational Molecular Biology | ||
| Deep Learning in Neuroethology | ||
| Big Data Neuroscience Ideas Lab | ||
| Deep Learning in Brains, Minds and Machines | ||
| Language Processing in Humans and Machines | ||
| History of Artificial Intelligence | ||
| Statistical Neuroscience | ||
| One Course in Artificial Intelligence: | 1 | |
| Artificial Intelligence | ||
| Machine Learning | ||
| Computer Vision | ||
| Computational Linguistics | ||
| Deep Learning | ||
| Machine Learning: from Theory to Algorithms | ||
| Two Upper-Level Neuroscience Electives | 2 | |
| 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. | ||
| The Neural Bases of Cognition | ||
| Neuroaesthetics and Reading | ||
| Neuroengineering | ||
| Neurobiology of Learning and Memory | ||
| Structure of the Nervous System | ||
| The Diseased Brain: Mechanisms of Neurological and Psychiatric Disorders | ||
| One Elective in Ethics: | 1 | |
| Computers, Freedom and Privacy | ||
| Fairness in Automated Decision Making | ||
| Data, Ethics and Society | ||
| Social Impact of Emerging Technologies: The Role of Scientists and Engineers | ||
| Ethics of Digital Technology | ||
| Ethics and Politics of Data | ||
| Race and Gender in the Scientific Community | ||
| Race, Gender, and Technology in Everyday Life | ||
| Two Additional Electives: | 2 | |
| 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. Students can substitute TWO semesters of independent study (NEUR1970 or equivalent course from another department) for one elective course | ||
| Introduction to Scientific Computing | ||
| Introduction to Modeling | ||
| Applied Partial Differential Equations I | ||
| Quantitative Models of Biological Systems | ||
| Introduction to Computational Linear Algebra | ||
| Applied Dynamical Systems | ||
| Statistical Inference II | ||
| Computational Probability and Statistics | ||
| Information Theory | ||
| Recent Applications of Probability and Statistics | ||
| Graphs and Networks | ||
| Pattern Theory | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Methods in Informatics and Data Science for Health | ||
| Brain Damage and the Mind | ||
| Language and the Mind | ||
| Linear Algebra for Machine Learning | ||
| Theory of Computation | ||
| Design and Analysis of Algorithms | ||
| Data Science | ||
| Topics in Optimization | ||
| Probability | ||
| Biological Physics | ||
| NEUR 1900 | Capstone | 1 |
| Total Credits | 14 | |