Computational Neuroscience
Spring 1998
Bard Ermentrout
Walt Schneider
Coordinates:
 Lectures:
 Wed 12:30 PM  1:50 PM Benedum 1222
 Thur 11:00 AM  12:30 PM Cathedral of Learning 237
Instructors:
Bard
Ermentrout
Walt Schneider
Abstract
The course offers an introduction to modeling methods in neuroscience. It
illustrates how models can extend and evaluate neuroscience concepts.
Basic techniques of modeling biophysics, excitable membranes, small network
and large scale network systems will be introduced. The course begins with
a consideration of mathematical models of excitable membranes, including
the HodgkinHuxley model and simplifications such as the MorrisLecar and
FitzHughNagumo models. It will provide handson laboratory experience in
modeling membranes, neurons, and neural networks. The course explores the
use of differential equations, numerical simulation, and graphical
techniques for modeling compartmental and connectionist neural systems.
The range of topics include simulations of electrical properties of
membrane channels, single cells, neuronal networks and connectionist
simulation. Students will be afforded laboratory experience in computer
modeling, and they will develop computational neuroscience models in the
course. Prerequisites for the course include basic knowledge of calculus,
neuroscience, and some computer programming.
Required texts: None
Recommended Texts:
 Bower, J. M. & Beeman, D. (1995) The Book of GENESIS New York
SpringerVerlag.
 Johnston, D. & Wuu, S., (1994) Foundations of cellular neurophysiology.
Cambridge MA: MIT Press20
 Kandel, E. R., Schwartz, J. H. & Jessell, T. M. (1991) Principles of Neural
Science. New York: Elsevier.
 Koch, C & Segev, I (1989) Methods in neuronal modeling: From synapses to
networks. Cambridge MA: MIT press (paper back edition).
 Shephard, G. M. (1990) The synaptic organization of the brain New York:
Oxford University Press. (paper back edition)
 White, E. L. (1989) Cortical Circuits: Synaptic organization of the cerebral
cortex structure, function, and theory. Boston: Birkhauser,
Syllabus
 Goals and Math review ( E) 12 class periods
 Why model
 What you learn
 Levels of modeling
 Don't view simulators as black boxes
 Getting intuition as to what the models will do
 How to translate behavior into ODE
 Math Review
 Review of linear odes
 Review of matrices and eigenvalues

Optional info on xpp and linear/nonlinear diffeqs
 Passive Membrane (E) 2 periods
Optional Readings: Chap. 5 B& B; handouts
 Introduction
 compartment
 dendrites
 resting potentials
 cable
 Modeling passive membranes (Chap. 3 B&B)
 equations for single compartment
 multicompartment model
 cable theory
 Computer implementation (E & S) 4 periods
 UNIX workshop for students not used to UNIX (Handouts) (special session)
 operating in UNIX
 editing
 simulation methodology
 phaseplane simulation environment (handouts)
 branching structure of a dendrite Genesis cable and two neurons (Chap.
2,3,1012 B&B)
 Active membranes basics (E & S) 3 periods
{Readings: Rinzel & Ermentrout Chap. 5 K&S; Chap. 4 B& B; handouts}
 Synapses 2 periods
{Readings: Chap. 6,McCormick Chap.2 in Shepherd 1990}
 Channels
 model basics
 Kinetic theory (Mainen and Sejnowski)
 GABA_A vs GABA_B
 second messengers
 Multiple levels of simulation
 Numeric approximation
 Simulating channel effects
 Example models Brown, others
 Full cell simulation (E & S) 1 period
{Readings: K&S Chap. 4; Chap. 14 B&B}
 Implementing multiple channels
 Determining empirical parameters
 Modeling the cortical microcircuit
 Population modeling (S) 2 periods
{ Readings Wilson & Bower Chap. 9 K&S; Chap. 17 B&B; Chap. 3&4 White;
Douglas & Martin 1992) }
Please grab this lengthy paper for the next
several lectures.
 Simplifying systems
 specialization
 receptive fields
 columns
 regions
 laminar distribution
 Identifying cell types
 Building up the system
 Interconnecting cells
 Examining population behavior
 Try these little networks!
 CLASS EXAM 1 period
 Here are the exam odes EXAM1.ODE and
EXAM2.ODE
 Simplified biological modeling
 Population activity in barrel fields (Dan Simons) 1 period
 using biological input and outputs
 problems of scaling
 how much and what kind of detail
 Connectionist modeling with a biological interest (Jay McClelland David
Plaut) 5 periods
 Introduction to PDP: philosophy, framework learning rules: Hebb, comp.
learn, contrast Hebb, BP relation biological synaptic modification
 Perception and models of perception/attentional disorders
 interactive activation model, stochastic version
 relation to Zipser's work relating network to neural variables
 Mosher & Behrmann and other models of neglect and other phenomena
 Development, language acquisition, language processing
 why PDP central to not only biological but also functional issues in
development
 Hebbian models of biological development (Linsker, Miller, etc.)
 PDP models of cognitive and language development and interpretation of
developmental and acquired disorders of reading and language
 Role of hippocampus in learning and memory. (Gluck, Myers, Schmajuk,
DiCarlo & others)
 Testing models (E & S)
 Getting parameters from the empirical data
 Prediction of basic effects and robustness
 acceptance criteria
 predictions of model
 sensitivity analysis
 Neuron Simulators (S)
 Neuron 2 periods
 single cell simulation
 small networks
 Genesis 1 periods
 general nature of the tool
 Other models
 Role of Simulation versus analysis
 conceptualization benefits
 computational benefits
 REVIEW SESSION 1 period
 IN CLASS Final 1 period
 Projects