BME 565 /BME 665 - Introduction to Computational Neurophysiology

Instructor(s): Patrick Roberts, Tamara Hayes

Time and Location:
CHCC 12181
01/09/07 - 03/15/07, Tuesday, Thursday 9:00 AM - 10:30 AM
This class will be videoconferenced (real time) to the West Campus, Central 123
Theoretical Neuroscience (2001, 2005) Peter Dayan & L. F. Abbott

Also to be used (available in the library):
Biophysics of Computation: Information Processing in Single Neurons (1998) Christof Koch
Spiking Neuron Models (2002) Wulfram Gerstner & Werner M. Kistler
Spikes: Exploring the neural code (1999) Fred Rieke et. al.
Dynamical Systems in Neuroscience (2007) Eugene M. Izhikevich

Strongly recommended if you need a math review:
The Nature of Mathematical Modeling (1999) Neil Gershenfeld

The student version of MATLAB is available here.

Class mailing list: To subscribe/unsubscribe, click here.

Our students: Summary of the background of students in the 2004 class.

Class notes, slides, and code:

Section A: Biophysical models of single neurons
Background and course introduction
Slides(pdf)   Video of class (requires RealPlayer to view)
Readings: Chapter 5.1-5.4 (Dayan: Neuroelectronics). Optional: Chapter 1 (Gerstner).
Also, please install Matlab if needed, read math reviews if needed. Handouts
Hodgkin-Huxley model, activation/inactivation dynamics

Slides(pdf)   Video of class
Class simulation movie. NEURON code:01_soma.hoc,
Readings: Chapter 6.3-6.6 (Dayan: Conductances and Morphology). Articles: Hodgkin52, Meunier02
Homework 1: Assignment (Sample answers), Matlab code: hh_integrate.m, hh_run.m
Graphical interface for Hodgkin-Huxley model:

Compartmental models, cable properties, spike propagation
Slides(pdf)     Video of class
Readings: Chapter 6.1-6.2 (Dayan: Channels).
Articles: Magee02, Hausser03
The channel zoo: K-channels
CLASS CANCELLED (weather) - please complete readings from Tuesday's class; we will cover all the material on channels in the Jan 23rd class.
More channel zoo: Ca+K, firing frequency adaptation
Slides(pdf)     Video of class
Readings: Chapter 5.8 (Dayan: Synaptic Conductances).
Articles: Marder02
Homework 2: Assignment
Synapses and receptors

Slides(pdf)   Video of class
Readings: Chapter 8.1 (Dayan: Synaptic Plasticity).

Biophysical models of synaptic plasticity; NMDA models

Slides(pdf)  Video of class
NEURON code:
Readings: Chapter 8.2 (Dayan: Synaptic Plasticity Rules).
Articles: Kennedy05, Fischer00, Spine movies (from Fischer00)

Spike-timing dependent synaptic plasticity
Slides(pdf)   Video of class
Homework 3: (word) (pdf)
Central pattern generators
Slides(pdf) Video of class
Matlab code:, Tritonia.m
Readings: Chapter 5.4 (Dayan: Integrate-and-Fire Neurons).
Articles: Buono01, Katz90
Section B: Information coding: Simplifying the model
Spiking neurons: integrate-and-fire model
Slides(pdf) Video of class
Matlab code: plot_2dDEq_FH.m,
Readings: Chapter 7.4 (Dayan: Recurrent Networks).
Articles: Izhikevich04, Naundorf06
Visiting lecturer: 11:30am  11
Mark Goldman

"The Oculomotor Integrator as a Model for Short-Term Memory: A Computational Investigation"
Neurological Sciences Institute, Rm 1100
OHSU West Campus (directions)
Videoconferenced to BSAC 0501C-CROET
Articles: Brody03, Goldman03
Exam 1 (due Feb 20th) (word) (pdf)

SRM model: coding with SRM
Slides(pdf)  Video of class
Class demo: Spiking Neurons
Readings: Chapter 1.2-1.6 (Dayan: Spike Statistics).
Articles: Jolivet04, Jensen01, Burkitt06
Spike trains: probabilistic firing and noise in spiking neurons
Slides (pdf) Video of class
Matlab code: HHnoise.m.
Readings: Dayan Ch. 1,2
Articles: berry99
Exam 1 due
Cascade models, rate codes
Slides (pdf) Video of class
Readings: Dayan Ch. 3
Articles: jazayeri06
Homework 4 (pdf)
Population (de)coding, exam review
Slides (pdf)   Video of class
Readings: Dayan 4
Articles: butts06
Information theoretic approaches
Slides (pdf) Video of class
Readings: Dayan 7.5, also Gerstner Ch. 8
Homework 4 due, Project definition due
Phase plane analyses
Slides (pdf) Video of class
FH_run.m, fhp.m, WCoscillator.m
Readings: Dayan 9
Exam 2 (word) (pdf)
Section C: Models of synaptic plasticity
Intro to adaptation and learning
Slides (pdf) Video of class
Readings: Dayan ch. 8
Articles: malach94
Models of cortical organization and learning
Slides (pdf) Video of class
Exam 2 due
Modeling in the real world: Sensory adaptation of mormyrid electric fish
Video of class
Exam week - no classes

Project report due by start of class.

Additional readings

Date Reading
Jan 9 Linear Algebra Primer
Facts about the brain
Background on differential equations
MATLAB Reference Manual (also available as Help in MATLAB)


Due Assignment
Jan 23 Homework 1: exploring activation/inactivation dynamics with Matlab. (Word)
Sample answers
Jan 30 Homework 2: examining the cable equation, and modeling more complex neuron properties.
Feb 8 Homework 3: synaptic plasticity
Feb 20 Exam 1: biophysical models of single neurons (due Feb 20th) (pdf)
Mar 1 Homework 4: Simple and noisy model neurons
Mar 13 Exam 2 (pdf) Sample Answers

Course Project

In this project you will go through the exercise of developing a model to answer a neuroscience question, by replicating and extending a previously-published modeling effort. You will be required to research a problem; define the hypothesis in the context of previous work; download, modify, and interpret an appropriate model; and explain the model findings and what you learned beyond what was presented in the original research.

The choice of problem to model can come from ModelDB, or you may identify a problem from your own research.

Details are available here: (word) (pdf)

Due Deliverable
March 1st Project definition
March 15th Final report

Grading Policy

Homework assignments will be graded pass/fail. Students are expected to complete all homework assignments successfully.

Late assignments will be accepted only with prior approval from the professors.

The mark in the course will be based on successful completion of the homework, and the result of 3 tests (one following each section of the course). Each test will be graded based on its completeness, clarity, and demonstrated depth of understanding. Grades will be assigned as follows:
4   Work and presentation are superior
3   Work is accurate and its presentation is of high quality
2   Work is complete, but there are some problems with its presentation or accuracy
1   Work is incomplete, inaccurate, or its presentation is poor
0   Work was not submitted

Final grades in the course will be determined based on the assessment criteria above. There will be no curve. Final grades have the following meanings:
A+   Superior performance in all aspects of the course with work exemplifying the highest quality.
A   Superior performance in most aspects of the course; high quality work in the remainder.
A-   High quality performance in all or most aspects of the course.
B+   High quality performance in some of the course; satisfactory performance in the remainder.
B   Satisfactory performance in the course.
B-   Satisfactory performance in most of the course, with the remainder being somewhat substandard.
C   Evidence of some learning but generally marginal performance.

Course Description

In this course students will explore how neurons communicate through electrical signals, how information transmission between neurons occurs, and how connectivity between neurons determines activity patterns and results in specialized behavior. This course uses a hands-on approach to develop and explain concepts from computational neurophysiology. The course has two goals: to help students understand how computational models can be used to analyze, explain and predict the physiological behavior of neurons and assemblies of neurons; and to provide students with an opportunity to use current research tools to investigate the concepts underlying these computational models. The course will include a very brief review of relevant concepts from cellular neurophysiology (action and membrane potentials, channels, etc.) and of mathematical concepts needed to understand the material.

Topics to be covered include Hodgkin-Huxley models of simple and complex morphologies; central pattern generators; models of simple invertebrate circuits; integrate-and-fire and spike-response neuron models for use in network models; models of neural development, ocular dominance and orientation columns; and rate versus spike-timing dependent plasticity.

Who should take this course?

This course will be of interest to students in engineering and mathematics with an interest in neuronal modeling, as well as to neuroscientists who would like to understand more about the role of computational models in neurophysiology. A solid math background is needed; some programming (in MATLAB) will be required.