Download BN4402 - ECE@NUS

BN4402 : To be offered in July 2003 for
ECE undergraduate and graduate
students.
Pre-requisite NIL
Scope: A study of Electrical Engineering
tools for Neuro-electrophysiology!
Tutorials: Based on NEURON Software
Package
This course allows students to familiarize with the evolving field of Neuroengineering
and introduces the concepts of Neuronal modeling. Neuronal Modeling is a technique
that Computational Neuroscientists use to explore the behavior of neurons. Typically invitro experiments are conducted on brain slices and cultured neurons to record specific
aspects of neuronal behavior. This data is then applied to a simulation model of the
neuron. Our Neuroengineering lab has facilities for doing such in vitro experiments. The
data will be supplied by the medical and bioengineering students working on in vitro
experiments.
What has motivated me to introduce this topic to our ECE students is the massive
requirement for computational neuroscientists both in industry and research. Recently, I
have found many research groups in the US wanting to work with computational
neuroscientists. The reason being that many companies are now plunging into the fanciful
area of Neuroengineering. Some of the feats that has attracted me to this area are the
needs to explore the working principle of implantable deep brain stimulators for epilepsy
and Parkinson’s disease. Some of these are FDA approved products. Computational
modeling of deep brain stimulation is one of the most recent and challenging topic for
research in the area of Neuroengineering!
In recent years the greater availability of workstations has resulted in significant increases
in modeling in many scientific disciplines. In Computational Neuroscience, there has
been an increase in the number, and complexity of models of single neurons, and neural
networks (Bower and Koch 1992). Modeling is attractive because it provides a deeper
understanding of what is still unknown about the system, and thus helps us to guide our
experiments so that we avoid generating massive amounts of unconnected and
uninterpretable data (Bower 1992).
A neuron generally consists of a central cell body or soma connected to a number of input
elements (called dendrites) and a single output element (called an axon).
This course will enable us to devise new approaches for visualizing the enormous
amounts of data generated from complex neuronal network simulations. We will be using
a package called “NEURON” to solve problems in the class. The problems will lead us to
perform Realistic Modeling of Brain Structures with Remote Interaction between
Simulations of the different neuronal structures in the brain.
Cable Theory
Cable Theory and the application of its equations describe the flow of current in an
electrical cable. Similarly, it characterizes the flow of heat in a rod and the diffusion of
substances in a solute, but more profoundly to neuroscientists, it describes the passage of
current in dendritic neurons.
Although this theory has made exact quantitative formulations of neurophysiological
events feasible, its use in modeling real neurons is limited because of its shear complexity
when dealing with neurons with comprehensive branching structures.
Compartmental Modeling
Compartmental Modeling is a recent extension to cable theory which involves
decomposing the dendritic branches of a neuron into individual compartments. Since
each compartment is kept small enough, only simple equations are needed to express it.
There are generally two types of compartments (passive and excitable compartments)
both of which can be modeled as electrical circuits.
Common to both types of compartments is the part of the circuit that describes current
flow through the cell membrane and to other compartments over a cytoplasmic resistance
Ri. The membrane assumes some capacitance CM, and there is a potential source E. The
circuit for an excitable compartment contains additional elements to simulate Sodium
(Na), Potassium (K) conductances as well as synaptic channels that impart the dendrite.
References
Bower, J. M. 1992. ``Modeling the Nervous System.'' Trends in Neuroscience 15, no.
11[173] (Nov.): 411-412.
Bower, J. M. and C. Koch. 1992. ``Experimentalists and modelers: can we all just get
along?'' Trends in Neuroscience 15, no. 11[173] (Nov.): 458-461.