NEUROSC C151 – Computational Neuroscience for Interdisciplinary Scientists (Ongoing)
NEUROSC C151 is a highly interdisciplinary course in computational neuroscience. This course is designed for undergraduate and graduate students in both experimental and computational tracks to acquire significant breadth and depth in computational neuroscience. Unlike others in this area, this course integrates data-driven modeling, simulations, and analyses of neural dynamics to train students in hypothesis-driven approach to computational modeling. Students can immediately apply the knowledge and skills from this course in research or industry. This course is also suitable for the CaSB, Cognitive Science and physical sciences/engineering students who wish to gain a reasonable understanding of modeling the brain. The unique module-based course structure utilizes state-of-the-art instructional tools for lectures, discussions, projects, and assessments.
The course features include:
- Emphasis on Neurobiology, Complex Systems, Mathematical Modeling and Computer-based Simulation Experiments to test, validate and use models for scientific investigations. Course content is free from obsessive notational Math and Computer Science.
- Module-based content ties the interdisciplinary concepts and their application through the use of computer-based modeling and simulations. This approach makes the course content discrete, scalable, and up-to-date with scientific advancements.
- A computational laboratory in which students develop computational models, conduct computer simulations, learn data analyses and visualization with emphasis on best practices. Python programming language and established Neuroscience modeling software for hands-on model simulation and analyses.
- Learning pods to cultivate collaborative competences and to maximize skill acquisition through peer instruction.
LS35 Computer Programming for Life Scientists (Ongoing)
In order to tackle the technological aspects of the life sciences, biologists require substantial training in computer technology, programming, and automation of data acquisition and analysis. This course integrates these essential topics with knowledge of biological systems in lecture, lab, and project formats. Students with and without programming background will benefit from the interdisciplinary structure of the course and the collaborative projects. They can immediately apply the learned knowledge and skills in research or industry. The class will consist of introductions to programming concepts/techniques and associated biological problems, followed by hands on computing exercises. The course will be taught using the widely used, object-oriented, high-level language Python. No programming background is required.
LS20 Quantitative Concepts for the Life Sciences (2015 – 2017)
This course was an interdisciplinary Math-Biology course, for incoming first year students. The course design emphasized student-centered approach thematic modules to enhance the traditional classroom format for Math courses. The themes consisted of biological case studies developed by the NSF-funded ‘Consortium of Mathematics and its Applications’ and were available to students online for free. Course features included inclusive instructional methodologies such as group activities, iClickers, online self-learning and self-mastery tools, computer labs and peer discussions, to elevate student motivation and appreciation for Math. The course content encompassed pre-calculus math ranging from basic statistics, mathematical functions and foundational concepts for calculus, all of which were taught with applications to different areas of Life Sciences. A key objective of LS20 was to develop and emphasize quantitative reasoning, critical thinking and foundational computer-based modeling and simulation for first year life sciences students. This course further strived to alleviate math anxiety and prepared students for subsequent LS courses.