Introduction to Theoretical Neurobiology: Linear cable theory and dendritic structure PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Introduction to Theoretical Neurobiology: Linear cable theory and dendritic structure PDF full book. Access full book title Introduction to Theoretical Neurobiology: Linear cable theory and dendritic structure by Henry Clavering Tuckwell. Download full books in PDF and EPUB format.
Author: Henry Clavering Tuckwell
Publisher:
ISBN:
Category : Mathematical models
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Henry Clavering Tuckwell
Publisher:
ISBN:
Category : Mathematical models
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Henry Clavering Tuckwell
Publisher:
ISBN:
Category : Mathematical models
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Henry C. Tuckwell
Publisher: Cambridge University Press
ISBN: 9780521022224
Category : Science
Languages : en
Pages : 304
Get Book Here
Book Description
The human brain contains billions of nerve cells whose activity plays a critical role in the way we behave, feel, perceive, and think. This two-volume set explains the basic properties of a neuron--an electrically active nerve cell--and develops mathematical theories for the way neurons respond to the various stimuli they receive. Volume 1 contains descriptions and analyses of the principle mathematical models that have been developed for neurons in the past thirty years. It provides a brief review of the basic neuroanatomical and neurophysiological facts that will form the focus of the mathematical treatment. Tuckwell discusses the mathematical theories, beginning with the theory of membrane potentials. He then goes on to treat the Lapicque model, linear cable theory, and time-dependent solutions of the cable equations. He concludes with a description of Rall's model nerve cell. Because the level of mathematics increases steadily upward from Chapter Two some familiarity with differential equations and linear algebra is desirable.
Author: Henry Clavering Tuckwell
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Henry C. Tuckwell
Publisher: Cambridge University Press
ISBN: 9780521352178
Category : Mathematics
Languages : en
Pages : 292
Get Book Here
Book Description
The second part of this two-volume set contains advanced aspects of the quantitative theory of the dynamics of neurons. It begins with an introduction to the effects of reversal potentials on response to synaptic input. It then develops the theory of action potential generation based on the seminal Hodgkin-Huxley equations and gives methods for their solution in the space-clamped and nonspaceclamped cases. The remainder of the book discusses stochastic models of neural activity and ends with a statistical analysis of neuronal data with emphasis on spike trains. The mathematics is more complex in this volume than in the first volume and involves numerical methods of solution of partial differential equations and the statistical analysis of point processes.
Author: Henry Clavering Tuckwell
Publisher: Cambridge University Press
ISBN: 0521350964
Category : Mathematical models
Languages : en
Pages : 307
Get Book Here
Book Description
Explaining the basic properties of a neuron, this volume develops mathematical theories for the way neurons respond to the various stimuli they receive. It contains descriptions and analyses of the principal mathematical models, providing a brief review of the basic neuroanatomical and neurophysiological facts with the mathematical theories.
Author: Henry C. Tuckwell
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Eugene M. Izhikevich
Publisher: MIT Press
ISBN: 0262514206
Category : Medical
Languages : en
Pages : 459
Get Book Here
Book Description
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 students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum—or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.
Author: Thomas Trappenberg
Publisher: Oxford University Press
ISBN: 0199568413
Category : Mathematics
Languages : en
Pages : 417
Get Book Here
Book Description
The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. Completely redesigned and revised, it introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain.
Author: Paul Miller
Publisher: MIT Press
ISBN: 0262347563
Category : Science
Languages : en
Pages : 405
Get Book Here
Book Description
A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.
