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
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Class mailing list: bme665@bme.ogi.edu.
To subscribe/unsubscribe, click here.
Our students: Summary of the background of students in the 2004 class.
Section A: Biophysical models of single neurons 
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Background and course introduction 1 
Slides(pdf)
Video of class (requires RealPlayer to view)
Readings:
Chapter 5.15.4 (Dayan: Neuroelectronics). Optional: Chapter 1 (Gerstner).
Also, please install Matlab if needed, read math reviews if needed.
Handouts

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HodgkinHuxley model, activation/inactivation dynamics 2 
Slides(pdf)
Video
of class
Class simulation movie. NEURON
code:01_soma.hoc, 01_soma.ses
Readings: Chapter 6.36.6 (Dayan: Conductances
and Morphology).
Articles: Hodgkin52, Meunier02
Homework 1:
Assignment
(Sample answers),
Matlab code: hh_integrate.m,
hh_run.m
Graphical interface for HodgkinHuxley model: hh_gui.zip

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Compartmental models, cable properties, spike propagation 3 
Slides(pdf)
Video of class
NEURON code:simulCode.zip
Readings: Chapter 6.16.2 (Dayan: Channels).
Articles: Magee02, Hausser03

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The channel zoo: Kchannels 4 
CLASS CANCELLED (weather)  please complete readings from Tuesday's class; we will cover all the material on channels in the Jan 23rd class.

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More channel zoo: Ca+K, firing frequency adaptation 5 
Slides(pdf)
Video of class
NEURON code:simulCode.zip
Readings: Chapter 5.8 (Dayan: Synaptic Conductances).
Articles: Marder02
Homework 2:
Assignment 
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Synapses and receptors 6 
Slides(pdf)
Video of class
NEURON code:syn_demo.zip
Readings: Chapter 8.1 (Dayan: Synaptic Plasticity).
Articles:Destexhe98a

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Biophysical models of synaptic plasticity; NMDA models 7 
Slides(pdf) Video
of class
NEURON code: nmdaCa.zip
Readings: Chapter 8.2 (Dayan: Synaptic Plasticity Rules).
Articles: Kennedy05, Fischer00, Spine
movies (from Fischer00)

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Spiketiming dependent synaptic plasticity 8 
Slides(pdf)
Video
of class
Homework 3:
(word)
(pdf) 
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Central pattern generators 9 
Slides(pdf) Video
of class
Matlab code: hh_Ca_Zach.zip, Tritonia.m
Readings: Chapter 5.4 (Dayan: IntegrateandFire
Neurons).
Articles: Buono01, Katz90 
Section B: Information coding: Simplifying the model 
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Spiking neurons: integrateandfire model 10 
Slides(pdf) Video
of class
Matlab code: plot_2dDEq_FH.m, 2dimIF.zip
Readings: Chapter 7.4 (Dayan: Recurrent Networks).
Articles: Izhikevich04, Naundorf06 
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Visiting lecturer: 11:30am 11
Mark Goldman 
"The Oculomotor Integrator as a Model for ShortTerm Memory: A Computational
Investigation"
Neurological Sciences Institute, Rm 1100
OHSU West Campus (directions)
Videoconferenced to BSAC 0501CCROET
Articles: Brody03, Goldman03
Exam 1 (due Feb 20th)
(word)
(pdf)

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SRM model: coding with SRM 12 
Slides(pdf) Video
of class
Class demo: Spiking Neurons
Readings: Chapter 1.21.6 (Dayan: Spike Statistics).
Articles: Jolivet04, Jensen01, Burkitt06 
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Spike trains: probabilistic firing and noise in spiking neurons 13 
Slides (pdf)
Video of class
Matlab code: HHnoise.m.
Readings:
Dayan Ch. 1,2
Articles:
berry99
Exam 1 due

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Cascade models, rate codes 14 
Slides (pdf)
Video
of class
Readings:
Dayan Ch. 3
Articles:
jazayeri06
Homework 4 (pdf) 
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Population (de)coding, exam review 15 
Slides (pdf)
Video
of class
Readings:
Dayan 4
Articles:
butts06

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Information theoretic approaches 16 
Slides (pdf)
Video
of class
Readings:
Dayan 7.5, also Gerstner Ch. 8
Homework 4 due, Project definition due

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Phase plane analyses 17 
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 
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Intro to adaptation and learning 18 
Slides (pdf)
Video
of class
Readings:
Dayan ch. 8
Articles:
malach94

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Models of cortical organization and learning 19 
Slides (pdf)
Video of
class
Exam 2 due 
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Modeling in the real world: Sensory adaptation of mormyrid electric fish 20 
Video of
class 
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Exam week  no classes 
Project report due by start of class.

In this project you will go through the exercise of developing a model to answer a neuroscience question, by replicating and extending a previouslypublished 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 
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. 
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 handson 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 HodgkinHuxley models of simple and complex
morphologies; central pattern generators; models of simple invertebrate circuits;
integrateandfire and spikeresponse neuron models for use in network models;
models of neural development, ocular dominance and orientation columns;
and rate versus spiketiming dependent plasticity.
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.
