Download Principles of Computational Modeling in NeuroscienceDavid Sterratt

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Advance Access published on 15 March 2012
doi:10.1093/bib/bbs003
Book Review
Principles of Computational Modeling
in Neuroscience
David Sterratt, Bruce Graham, Andrew
Gillies and David Willshaw
Cambridge University Press,
Cambridge, UK; 2011
ISBN: 978-0-521-87795-4;
Hardback; 390 pp.
Understanding complex neurobiological systems is
one of the most difficult challenges in modern science. This book is focused on computational neuroscience, which provides a mathematical foundation
and a rich set of computational approaches for
understanding the principles and dynamics of the
nervous system. Taking a bottom up strategy, the
book offers comprehensive, step-by-step coverage
on how to model the neuron and neural circuitry
to understand the nervous system at multiple levels,
from ion channels to networks. It has arisen from
graduate courses to students in physical, mathematical and computer sciences, and can serve as an
excellent text for courses in computational
neuroscience.
The book consists of 11 chapters and 2 appendices. Chapter 1 gives an overview of the book.
Chapter 2 provides the basis for the development
of neuronal models by introducing primary electrical
properties of neurons. It starts with an introduction
to the neuronal membrane, and then discusses physical basis of ion movement in neurons, including
electrical drift and diffusion. The Nernst equation
is introduced to model the equilibrium potential resulting from permeability to a single ion in the resting membrane. The Goldman–Hodgkin–Katz
equations and their simplifications are presented to
model membrane ionic with more than one type of
membrane-permeable ions. Then, the resistor–capacitor (RC) circuit is introduced to approximate the
equivalent electrical circuit of a patch of membrane.
The compartmental model is discussed to handle the
spatial extent of the membrane. Finally, the cable
equation is presented to model the spatiotemporal
evolution of the membrane potential.
Chapter 3 presents the first quantitative model, the
Hodgkin–Huxley (HH) model, for describing
the action membrane potential. After introducing
the basic concept of the action potential, it explores
how a mixture of physical intuition and curve-fitting
can be used to produce the HH mathematical
scheme, which models the squid giant axon
sodium and potassium voltage-gated ion channels.
The HH model is then used to simulate nerve
action potentials and is evaluated by a comparison
with empirical recordings. After considering the
temperature effect, it concludes with a discussion of
how to use the HH formalism to build models of ion
channels in general.
Chapter 4 explores how voltage spreads along the
membrane using multiple connected RC circuits,
referred to as compartmental modeling. It starts
with the construction of a compartmental model
by representing quasi-isopotential sections of neurite
(small pieces of dendrite, axon or soma) as compartments, which are simple geometric objects such as
spheres or cylinders. It then presents approaches for
using real neuronal morphology as the basis of the
model. After that, it considers in detail methods and
issues of parameter estimation for determining specific passive membrane properties using an electrical
model instead of direct measurement with experimental techniques that is often impractical. Finally,
it discusses the issues involved in incorporating active
channels into compartmental models.
Chapter 5 concentrates on the theory of modeling
different types of active ion channels, including voltage- and ligand-gated. It first reviews channel structure and function, ion channel nomenclature and
experimental techniques for model development.
Three formalisms are discussed for modeling ensembles of voltage-gated ion channels: the HH formalism, the thermodynamic formalism and the Markov
kinetic scheme. Methods for modeling ligand-gated
channels are discussed using calcium-dependent potassium channels as an example. Transition state
theory is introduced and applied to constrain the
rate coefficients in both the thermodynamic formalism and the Markov kinetic scheme.
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Book Review
Chapter 6 describes methods of modeling intracellular signaling systems with a focus on calcium. It
details how intracellular calcium concentration can
be modeled, and examines models for voltage-gated
calcium channels, membrane-bound pumps, calcium
buffers and diffusion. Examples of intracellular signaling pathways involving more complex enzymatic
reactions and cascades are also considered. The
well-mixed approach is used to model these pathways with sufficient presence of molecular species,
while stochastic methods are used for compartments
with small numbers of molecules. Spatial modeling is
discussed to handle spatially inhomogeneous systems.
Chapter 7 explores a range of models for chemical
synapses and also briefly discusses electrical synapses.
It first focuses on methods for modeling postsynaptic
responses using simple phenomenological waveforms
and more complex kinetic schemes, then models
presynaptic neurotransmitter release by considering
vesicle release and vesicle availability, and finally
forms complete synapse models by relating the arrival
of the presynaptic action potential with a postsynaptic response. Methods for modeling short-term dynamics and long-lasting synaptic plasticity are both
discussed.
Chapter 8 covers a spectrum of simplified models
of neurons by stripping complicated ones down to
their bare essentials. These models are computationally more efficient and are useful for incorporating
into networks. Three broad categories of neuron
models are discussed. The first category includes simplified multi-compartmental models with reduced
number of compartments and reduced number of
state variables. The second category includes the
integrate-and-fire model and related spike-response
model, where the membrane potential is left as the
only state variable. At the simplest end of the spectrum, the third category includes rate-based models,
which communicate via firing rates rather than individual spikes. Although highly simplified, these
models can reasonably represent neuronal activity
in certain areas of the nervous system and provide
insights into complex computations.
Chapter 9 examines a variety of network models
where neurons are modeled with different levels of
detail. It first presents methods for constructing the
feed-forward and recurrent networks of the associative memory, where a neuron is treated as a
two-state device and a synapse as a simple binary
or linear device. It then describes another method
to embed recurrent associative memory networks
391
in a network of excitatory and inhibitory integrateand-fire neurons, and next presents more complex
network models of conductance-based neurons
where associative memory can be embedded. After
that, it explores two different models of thalamocortical interactions: one with multi-compartmental
neurons, the other with spiking neurons. Finally, it
discusses multi-compartmental models of the basal
ganglia for elucidating the electrophysiological effects of deep brain stimulation.
Chapter 10 reviews examples of modeling methods in developmental neuroscience. It discusses
methods at the single neuron level, including developmental models for neuronal morphology and
physiology. It also examines modeling methods for
the development of the spatial arrangement of the
nerve cells within a neural structure and those for the
development of connectivity patterns (patterns of
ocular dominance, connections between nerve and
muscle, and retinotopic maps). Chapter 11 concludes
the entire book by a brief discussion of the history
and future directions of computational neuroscience.
This book has done a nice job of laying out their
strategy for covering major topics in the field of
computational neuroscience while maintaining a
well-organized structure. It is prepared for both
expert and non-expert readers with an elementary
background in neuroscience and some high school
mathematics. As a computer scientist working on
analyzing human brain data at the macro-scale level
and a non-expert in modeling the neuronal data at
the micro-scale level, I have found this book very
easy to follow and have enjoyed reading it. I really
like it that this book provides plenty of figures, tables
and boxes to assist with the presentation of the content in a very informative, intuitive and organized
manner, which I feel plays an important role in helping readers better understand the fundamental concepts, scientific problems and computational models.
In addition to appendices containing overviews and
links to relevant computational and mathematical resources, the authors maintain a book website that
provides another source of useful and up-to-date information. These resources are especially beneficial
when the book is used as a text for a course. The
code examples and links to external simulators and
databases could be very valuable to the preparation of
lecture notes and student exercises. It would be a
more desirable case if the authors could consider
including a collection of lecture slides and course
exercises at the website when available.
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Book Review
This book is also a good resource for people
working in relevant fields. For example, the NIH
Human Connectome Project is a newly launched,
landmark study that employs a neuroinformatics and
systems biology approach for understanding the
human brain as a complex network at multiple
levels, from genetic determinants to cellular processes
to the complex interplay of brain structure, function,
behavior and cognition. The network connectivity
of the human brain needs to be analyzed in a comprehensive fashion across all scales, from the
micro-scale of synaptic connections between neurons to the macro-scale of structural and functional
connections between anatomically distinct brain
regions. The concepts and modeling methods presented in this book are directly applicable to the
micro-scale analysis, and could also be very helpful
to the macro-scale analysis, since the knowledge
learned from the micro-scale analysis can help yield
biological insights and reveal mechanisms underlying
macro-scale phenomena.
In summary, this is a timely, well-written book
that provides a comprehensive, in-depth and stateof-the-art coverage of computational modeling in
neuroscience. It can serve as an excellent text for a
graduate level course in computational neuroscience,
as well as a valuable reference for experimental
neuroscientists, computational neuroscientists and
people working in relevant areas such as neuroinformatics and systems biology.
FUNDING
Supported in part by NSF IIS-1117335, NIH UL1
RR025761, U01 AG024904, NIA RC2 AG036535,
NIA R01 AG19771, and NIA P30 AG10133-18S1.
Li Shen
Center for Neuroimaging, Department of Radiology and Imaging
Sciences; Center for Computational Biology and Bioinformatics,
Indiana University School of Medicine, 950 W.Walnut St,
R2 E124, Indianapolis, IN 46202, USA
E-mail: [email protected]