Download single neuron computation neural networks foundations to applications in pdf or read single neuron computation neural networks foundations to applications in pdf online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get single neuron computation neural networks foundations to applications in pdf book now. This site is like a library, Use search box in the widget to get ebook that you want.



Single Neuron Computation

Author: Thomas M. McKenna
Publisher: Academic Press
ISBN: 1483296067
Size: 52.70 MB
Format: PDF, ePub, Mobi
View: 3467
Download and Read
This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure. The focus on cellular-level processes is twofold. From a computational neuroscience perspective, a thorough understanding of the information processing performed by single neurons leads to an understanding of circuit- and systems-level activity. From the standpoint of artificial neural networks (ANNs), a single real neuron is as complex an operational unit as an entire ANN, and formalizing the complex computations performed by real neurons is essential to the design of enhanced processor elements for use in the next generation of ANNs. The book covers computation in dendrites and spines, computational aspects of ion channels, synapses, patterned discharge and multistate neurons, and stochastic models of neuron dynamics. It is the most up-to-date presentation of biophysical and computational methods.

Biophysics Of Computation

Author: Christof Koch
Publisher: Oxford University Press
ISBN: 0195181999
Size: 46.61 MB
Format: PDF, ePub, Mobi
View: 1603
Download and Read
Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes.Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation.Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

Theoretical Mechanics Of Biological Neural Networks

Author: Ronald J. MacGregor
Publisher: Elsevier
ISBN: 0080924417
Size: 28.68 MB
Format: PDF, Kindle
View: 2399
Download and Read
Theoretical Mechanics of Biological Neural Networks presents an extensive and coherent discusson and formulation of the generation and integration of neuroelectric signals in single neurons. The approach relates computer simulation programs for neurons of arbitrary complexity to fundamental gating processes of transmembrance ionic fluxes of synapses of excitable membranes. Listings of representative computer programs simulating arbitrary neurons, and local and composite neural networks are included. Develops a theory of dynamic similarity for characterising the firing rate sensitivites of neurons in terms of their characteristic anatomical and physiological parameters Presents the sequential configuration theory - a theoretical presentation of coordinated firing patterns in entire neural population Presents the outlines of mechanics for multiple interacting networks in composite systems

Neural Network Design 2nd Edition

Author: Martin Hagan
Publisher:
ISBN: 9780971732117
Size: 31.16 MB
Format: PDF, ePub
View: 6446
Download and Read
This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems.

Advances In Swarm Intelligence

Author: Ying Tan
Publisher: Springer Science & Business Media
ISBN: 3642134971
Size: 35.78 MB
Format: PDF, Docs
View: 423
Download and Read
The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNCS has grown into the most comprehensive computer science research forum available. The scope of LNCS, including its subseries LNAI and LNBI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields. In parallel to the printed book, each new volume is published electronically in LNCS Online.

Temporal Coding In The Brain

Author: G. Buzsaki
Publisher: Springer Science & Business Media
ISBN: 3642851487
Size: 17.61 MB
Format: PDF, Kindle
View: 4550
Download and Read
Temporal coding in the brain documents a revolution now occurring in the neurosciences. How does parallel processing of information bind together the complex nature of the outer and our inner worlds? Do intrinsic oscillations and transient cooperative states of neurons represent the physiological basis of cognitive and motor functions of the brain? Some answers to these challenging issues are provided in this book by leading world experts of brain function. A common denominator of the works presented in this volume is the nature and mechanisms of neuronal cooperation in the temporal domain. The topics range from simple organisms to the human brain. The volume is intended for investigators and graduate students in neurophysiology, cognitive neuroscience, neural computation and neurology.

Energy Minimization Methods In Computer Vision And Pattern Recognition

Author: Marcello Pelillo
Publisher: Springer Science & Business Media
ISBN: 9783540629092
Size: 79.31 MB
Format: PDF, ePub, Docs
View: 2236
Download and Read
This book constitutes the refereed proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR'97, held in Venice, Italy, in May 1997. The book presents 29 revised full papers selected from a total of 62 submissions. Also included are four full invited papers and a keynote paper by leading researchers. The volume is organized in sections on contours and deformable models, Markov random fields, deterministic methods, object recognition, evolutionary search, structural models, and applications. The volume is the first comprehensive documentation of the application of energy minimization techniques in the areas of compiler vision and pattern recognition.

Mathematical Approaches To Neural Networks

Author: J.G. Taylor
Publisher: Elsevier
ISBN: 9780080887395
Size: 24.52 MB
Format: PDF
View: 5487
Download and Read
The subject of Neural Networks is being seen to be coming of age, after its initial inception 50 years ago in the seminal work of McCulloch and Pitts. It is proving to be valuable in a wide range of academic disciplines and in important applications in industrial and business tasks. The progress being made in each approach is considerable. Nevertheless, both stand in need of a theoretical framework of explanation to underpin their usage and to allow the progress being made to be put on a firmer footing. This book aims to strengthen the foundations in its presentation of mathematical approaches to neural networks. It is through these that a suitable explanatory framework is expected to be found. The approaches span a broad range, from single neuron details to numerical analysis, functional analysis and dynamical systems theory. Each of these avenues provides its own insights into the way neural networks can be understood, both for artificial ones and simplified simulations. As a whole, the publication underlines the importance of the ever-deepening mathematical understanding of neural networks.

Neural Networks And Analog Computation

Author: Hava T. Siegelmann
Publisher: Springer Science & Business Media
ISBN: 146120707X
Size: 35.60 MB
Format: PDF, ePub, Mobi
View: 1274
Download and Read
The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.

Theoretical Mechanics Of Biological Neural Networks

Author: Ronald J. MacGregor
Publisher: Elsevier
ISBN: 0080924417
Size: 32.89 MB
Format: PDF, ePub, Mobi
View: 4541
Download and Read
Theoretical Mechanics of Biological Neural Networks presents an extensive and coherent discusson and formulation of the generation and integration of neuroelectric signals in single neurons. The approach relates computer simulation programs for neurons of arbitrary complexity to fundamental gating processes of transmembrance ionic fluxes of synapses of excitable membranes. Listings of representative computer programs simulating arbitrary neurons, and local and composite neural networks are included. Develops a theory of dynamic similarity for characterising the firing rate sensitivites of neurons in terms of their characteristic anatomical and physiological parameters Presents the sequential configuration theory - a theoretical presentation of coordinated firing patterns in entire neural population Presents the outlines of mechanics for multiple interacting networks in composite systems