Computational Neuroscience

Overview

Course Description: Research techniques in computational neuroscience, including biophysical modeling of neurons, synapses, and neuronal networks. Topics include calcium dynamics, cellular homeostasis, ion channel kinetics, central pattern generator dynamics, neuronal network synchronization, and artificial intelligence. Students acquire basic programming skills in Matlab and other software to build/analyze computational models from literature-based biophysical measurements.

Learning Objectives

Students will learn how to build computational models using biophysical measurements reported in the primary literature. They will learn how to analyze neuronal models using Matlab.

Assessment

The progress of the students will be assessed through quizzes, homework assignments, and presentations of research articles. Students will be required to formulate and carry out a research project based on recent publications related to the course and their scientific interests. Each research project will have to formulate a novel data-driven hypothesis and test it in an appropriate model.  Students will submit two written progress reports and make two progress presentations, make the final presentation, and submit the final report. They will discuss not only the predictions of the theoretical findings but also the limitations. The students are encouraged to submit a manuscript and code to ReScience journal.

Primary textbook

David Sterratt, Bruce Graham, Andrew Gillies, David Willshaw (2011) Principles of Computational Modelling in Neuroscience. Cambridge University Press.
Other study materials and computer tutorials will be provided by the instructor, as needed.

Tentative Schedule of the topics

  • Introduction to Matlab, and computer modeling.
  • The basis of electrical activity in the neuron
  • The Hodgkin-Huxley model of the action potential
  • Compartmental models
  • Models of active ion channels, introduction to bifurcation analysis software Content
  • Intracellular mechanisms
  • The synapse
  • Simplified models of neurons
  • Networks of neurons. Models of Learning.
  • Synapses and synchrony
  • Realistic modeling of small neuronal networks, Central pattern generators, Brute force database approach, Neuromodulation