Fundamentals in computational neuroscience models (NSBV BC2004)

Course Description: 

Computational neuroscience is an exciting field that brings together theories and ideas from many different areas in STEM – including physics, math, computer science, psychology, chemistry, and of course, neuroscience. Through a combination of hands-on activities and interactive lectures, students will be introduced to a selection of quantitative concepts while exploring computational models of neuronal and neural network activity. Before beginning this course students are expected to have learned about the biological basis of the action potential and synaptic transmission (see prerequisites). By the end of the course, students will come away with an intuitive understanding of the quantitative underpinnings of computational neuroscience models and some basic coding principles. This course assumes NO prior coding experience. Students interested in the computational track for the Neuroscience major should consider taking this course right after taking Introduction to Neuroscience (NSBV BC 1001).

Learning Outcomes: Students will learn about and work with several of the foundational computational neuroscience models relating to single neuron and neural network activity. Students will be exposed to the mathematical and scientific principles behind those models, and will develop the confidence to pursue a deeper exploration of those topics. Specifically, students will learn to:

  • Identify the scope of a neuroscience model and determine what it can and cannot tell us.
  • Compare models and select an appropriate model for a given scientific question from among the models covered in this course.
  • Make connections from the action potential and synaptic transmission to quantitative/fundamental theories from other STEM disciplines (physics, chemistry, etc).
  • Implement computational neuroscience models of neurons and neural networks using Matlab.

Prerequisites: Introduction to Neuroscience (NSBV BC 1001), OR Introduction to Cellular and Molecular Biology (BIO BC 1502), OR permission of instructor. No prior coding experience is required.

Syllabus draft

Notes: The scope of this course is limited to computational models focused on the action potential, neuronal voltage activity, and synaptic transmission. It will not cover higher-level models of cognition, nor will we cover data analysis topics. If you’re interested in those topics, you may consider taking my Spring 2022 course called “The Neural Code”. Details coming soon.

NSBV BC2004 assumes no prior coding or Matlab experience and has no mathematics prerequisites. If you have already taken the calculus series, or advanced mathematics courses, or programming courses, my Fundamentals course may be too easy for you. Please consider taking a more advanced course such as:

NBHV G4360 Introduction to Theoretical Neuroscience

BMEB W4020 Computational Neuroscience: Circuits in the Brain