文档介绍：puter to Brain:
Foundations of
Computational
Neuroscience
William W. Lytton
Springer
for Jeeyune and Barry
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Foreword
In puter to Brain: Foundations putational Neuroscience,
William Lytton provides a gentle but rigorous introduction to the art
of modeling neurons and neural systems. It is an accessible entry to the
methods and approaches used to model the brain at many different levels,
ranging from synapses and dendrites to neurons and neural circuits. Dif-
ferent types of questions are asked at each level that require different types
of models.
Why learn this?
One of the reasons why someone might want to learn putational
neuroscience is to better predict the es of experiments. The process
of designing an experiment to test a hypothesis involves making predic-
tions about what the possible es of the experiment might be and to
work out the implications of each possible result. This is a difficult task in
most biological systems, especially ones like the brain that involve many
interacting parts, some of which are not even known. A model may reveal
assumptions about the system that were not fully appreciated.
One of the earliest and most essful models is the Hodgkin-Huxley
model of the action potential (Chap. 12). For their classic papers on the
giant squid axon, they integrated the differential equations on a hand-
powered mechanical calculator. Computers today are millions of times
faster than those in the 1950s and it is now possible to simulate cortical
neurons having thousands of partments and dozens of dif-
ferent types of ions channels, works with thousands of interacting
neurons. plex dynamics of works is exceptionally diffi-
cult to predict putational tools puter science, and
mathematical tools from dynamical systems theory.
But there is another reason to delve into this book. Computational neuro-
science also provides a framework for thinking about how brain mechanisms
give rise to behav