Aims and Scope
The undergraduate CNS option provides a foundation in math, physics, biology and computer science to prepare students for interdisciplinary graduate studies in neuroscience and career paths that involve computational applications inspired by properties of biological systems, such as artificial intelligence and computer vision. By graduation, students will have acquired knowledge in neurobiology, computation principles across different systems, methods used in modern neuroscience research, as well as the ability to critically evaluate and understand neuroscience literature, and be able to work in a team and communicate effectively.
To accomplish these goals, students are expected to complete a series of math and physics courses to establish solid quantitative skills. Then, they are expected to take two groups of courses, of which one has a biology focus, while the other has a CS focus. Through these courses, students are exposed to different sub-disciplines of neuroscience while also acquiring the quantitative skills needed in graduate research and industry jobs. Students will receive instruction in scientific communications through SEC 10 and SEC 11, SEC 12, SEC 13, or Bi/BE 24.
Undergraduate research is encouraged both during the academic year and through participation in summer research programs.
Students with a grade-point average lower than 1.9 will not be allowed to continue in the option except with special permission from the option representative.
CNS Option Requirements
- Fulfillment of extended core requirements in Differential Equations (Ma 2 or equivalent); Probability and Statistics (Ma 3 or equivalent); Waves (Ph 2a, Ph 12a or equivalent), Thermodynamics and Statistical Mechanics (Ph 2c, Ph 12c or equivalent).
- Demonstration of competency in computer programming or computer science by taking CS 1, CS 2, and one of BE 103 and CS 3 or by taking an approved alternative course, or by passing a placement exam administered by the computer science option.
- Bi/CNS 162 and 9 units of laboratory courses taken from the following list: CS/CNS 171, CS/CNS 174, EE 45, EE 90, EE 91 ab, ME 72 ab, ME 50ab, BE 107, BE/EE/MedE 189 a, BE/CS 196a, Bi/BE 227, Bi/CNS/BE/NB 230.
- ACM 95 ab, or Ma 108 abc, or Ma 109 abc.
- SEC 10 and SEC 11, SEC 12, or SEC 13; or Bi/BE 24.
- Bi 8, Bi 9, NB/Bi/CNS 150, Bi/CNS/ NB 157, Bi/CNS/NB 164.
- Choose five from the following list: EE 111, CS/CNS/EE 156 ab, CS/CNS/EE 155, CS 159, CNS/Bi/EE/CS/NB 186, CNS/Bi/Ph/CS/NB 187, BE/CS/CNS/Bi 191a, BE 150, IDS/ACM/CS 157.
- 45 units of electives chosen from either advanced EAS courses or advanced science courses offered by BBE, CCE, GPS, or PMA divisions.
CNS Typical Course Schedule
Units per term | ||||
1st | 2nd | 3rd | ||
First Year | ||||
CS 1 |
Introduction to Computer
Programming |
9 | - | - |
CS 2 | Introduction to Programming Methods | - | 9 | - |
First-Year Humanities | 9 | 9 | 9 | |
First-Year Core | 27 | 27 | 27 | |
Total | 45 | 45 | 36 | |
Second Year | ||||
Ph 2 ac | Waves, Statistical Mechanics | 9 | - | 9 |
Ma 2 | Sophomore Mathematics | 9 | - | - |
Ma 3 | Sophomore Mathematics | - | 9 | - |
ACM 95 ab | Intro. Methods of Applied Math | - | 12 | 12 |
EE 111 | Signals and systems | 9 | - | - |
Bi 8, 9 | Molecular, Cell Biology | - | 9 | 9 |
NB 150 | Introduction to Neuroscience | - | - | 10 |
HSS Electives | 9 | 9 | 9 | |
Electives | 9 | 9 | - | |
Total | 45 | 48 | 49 | |
Third Year | ||||
BE 103a | Introduction to Data Analysis in Biological Sciences | 9 | - | - |
BE 103b | Statistical Inference in the Biological Sciences | - | 9 | - |
Bi 164 | Tools in Neurobiology | 9 | - | - |
CS 156 a | Learning Systems | 9 | - | - |
Bi 162 |
Cellular and Systems
Neuroscience Lab |
- | 12 | - |
BE/CS 191 a | Comparative Nervous Systems | - | 9 | - |
+++++++++++++++ | Engineering Lab | - | - | 9 |
HSS Electives | 9 | 9 | 9 | |
Electives | 9 | 9 | 27 | |
Total | 45 | 48 | 45 | |
Fourth Year | ||||
CNS 187 | Neural Computation | 9 | - | - |
CNS 186 | Vision: From Computational Theory to Neuronal Mechanisms | - | 12 | - |
CS 159 | Advanced Topic in Machine Learning | - | - | 9 |
SEC 10, SEC 11-13 | Scientific Communication | 3 | - | 3 |
HSS Electives | 9 | 9 | 9 | |
Electives | 18 | 18 | 18 | |
Total | 39 | 39 | 39 |