|Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (Computational Neuroscience Series)|
by Eugene M. Izhikevich (Author)
Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition.
In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate...
|Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series)|
by Peter Dayan (Author), Laurence F. Abbott (Author)
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.
The book is divided into three parts. Part I discusses the relationship...
|Principles of Neural Science, Fifth Edition (Principles of Neural Science (Kandel))|
by Eric R. Kandel (Editor), James H. Schwartz (Editor), Thomas M. Jessell (Editor), Steven A. Siegelbaum (Editor), A. J. Hudspeth (Editor)
Now updated: the definitive neuroscience resource―from Eric R. Kandel, MD (winner of the Nobel Prize in 2000); James H. Schwartz, MD, PhD; Thomas M. Jessell, PhD; Steven A. Siegelbaum, PhD; and A. J. Hudspeth, PhD
A Doody's Core Title for 2017!
900 full-color illustrations
Deciphering the link between the human brain and behavior has always been one of the most intriguing―and often challenging―aspects of scientific...
|Rhythms of the Brain|
by Gyorgy Buzsaki (Author)
Studies of mechanisms in the brain that allow complicated things to happen in a coordinated fashion have produced some of the most spectacular discoveries in neuroscience. This book provides eloquent support for the idea that spontaneous neuron activity, far from being mere noise, is actually the source of our cognitive abilities. It takes a fresh look at the coevolution of structure and function in the mammalian brain, illustrating how self-emerged oscillatory timing is the brain's fundamental...
|Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Studies in Nonlinearity)|
by Steven H. Strogatz (Author)
This textbook is aimed at newcomers to nonlinear dynamics and chaos, especially students taking a first course in the subject. The presentation stresses analytical methods, concrete examples, and geometric intuition. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization,...
|Principles of Neural Design|
by Peter Sterling ; Simon Laughlin (Author)
|Analyzing Neural Time Series Data: Theory and Practice (Issues in Clinical and Cognitive Neuropsychology)|
by Mike X Cohen (Author)
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings.
This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG),...
|Deep Learning (Adaptive Computation and Machine Learning series)|
by Ian Goodfellow (Author), Yoshua Bengio (Author), Aaron Courville (Author)
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
-- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and...
|Principles of Neural Design (MIT Press)|
by Peter Sterling (Author), Simon Laughlin (Author)
Two distinguished neuroscientists distil general principles from more than a century of scientific study, "reverse engineering" the brain to understand its design.
Neuroscience research has exploded, with more than fifty thousand neuroscientists applying increasingly advanced methods. A mountain of new facts and mechanisms has emerged. And yet a principled framework to organize this knowledge has been missing. In this book, Peter Sterling and Simon Laughlin, two leading...
|Mathematical Foundations of Neuroscience (Interdisciplinary Applied Mathematics)|
by G. Bard Ermentrout (Author), David H. Terman (Author)
This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very...