Single Neuron Computation
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Single Neuron Computation
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Author : Thomas M. McKenna
language : en
Publisher: Academic Press
Release Date : 2014-05-19
Single Neuron Computation written by Thomas M. McKenna and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.
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
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Author : Christof Koch
language : en
Publisher: Oxford University Press
Release Date : 2004-10-28
Biophysics Of Computation written by Christof Koch and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-10-28 with Medical categories.
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.
Space Time Computing With Temporal Neural Networks
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Author : James E. Smith
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2017-05-18
Space Time Computing With Temporal Neural Networks written by James E. Smith and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-18 with Computers categories.
Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers. Not only are the brain's computational capabilities far beyond those of conventional computers, its energy efficiency is truly remarkable. This book, written from the perspective of a computer designer and targeted at computer researchers, is intended to give both background and lay out a course of action for studying the brain's computational paradigm. It contains a mix of concepts and ideas drawn from computational neuroscience, combined with those of the author. As background, relevant biological features are described in terms of their computational and communication properties. The brain's neocortex is constructed of massively interconnected neurons that compute and communicate via voltage spikes, and a strong argument can be made that precise spike timing is an essential element of the paradigm. Drawing from the biological features, a mathematics-based computational paradigm is constructed. The key feature is spiking neurons that perform communication and processing in space-time, with emphasis on time. In these paradigms, time is used as a freely available resource for both communication and computation. Neuron models are first discussed in general, and one is chosen for detailed development. Using the model, single-neuron computation is first explored. Neuron inputs are encoded as spike patterns, and the neuron is trained to identify input pattern similarities. Individual neurons are building blocks for constructing larger ensembles, referred to as "columns". These columns are trained in an unsupervised manner and operate collectively to perform the basic cognitive function of pattern clustering. Similar input patterns are mapped to a much smaller set of similar output patterns, thereby dividing the input patterns into identifiable clusters. Larger cognitive systems are formed by combining columns into a hierarchical architecture. These higher level architectures are the subject of ongoing study, and progress to date is described in detail in later chapters. Simulation plays a major role in model development, and the simulation infrastructure developed by the author is described.
Neuronal Dynamics
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Author : Wulfram Gerstner
language : en
Publisher: Cambridge University Press
Release Date : 2014-07-24
Neuronal Dynamics written by Wulfram Gerstner and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-24 with Computers categories.
This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Spiking Neuron Models
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Author : Wulfram Gerstner
language : en
Publisher: Cambridge University Press
Release Date : 2002-08-15
Spiking Neuron Models written by Wulfram Gerstner and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-08-15 with Computers categories.
Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed.
Neural Computation In Hopfield Networks And Boltzmann Machines
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Author : James P. Coughlin
language : en
Publisher: University of Delaware Press
Release Date : 1995
Neural Computation In Hopfield Networks And Boltzmann Machines written by James P. Coughlin and has been published by University of Delaware Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
"One hundred years ago, the fundamental building block of the central nervous system, the neuron, was discovered. This study focuses on the existing mathematical models of neurons and their interactions, the simulation of which has been one of the biggest challenges facing modern science." "More than fifty years ago, W. S. McCulloch and W. Pitts devised their model for the neuron, John von Neumann seemed to sense the possibilities for the development of intelligent systems, and Frank Rosenblatt came up with a functioning network of neurons. Despite these advances, the subject had begun to fade as a major research area until John Hopfield arrived on the scene. Drawing an analogy between neural networks and the Ising spin models of ferromagnetism, Hopfield was able to introduce a "computational energy" that would decline toward stable minima under the operation of the system of neurodynamics devised by Roy Glauber." "Like a switch, a neuron is said to be either "on" or "off." The state of the neuron is determined by the states of the other neurons and the connections between them, and the connections are assumed to be reciprocal - that is, neuron number one influences neuron number two exactly as strongly as neuron number two influences neuron number one. According to the Glauber dynamics, the states of the neurons are updated in a random serial way until an equilibrium is reached. An energy function can be associated with each state, and equilibrium corresponds to a minimum of this energy. It follows from Hopfield's assumption of reciprocity that an equilibrium will always be reached." "D. H. Ackley, G. E. Hinton, and T. J. Sejnowski modified the Hopfield network by introducing the simulated annealing algorithm to search out the deepest minima. This is accomplished by - loosely speaking - shaking the machine. The violence of the shaking is controlled by a parameter called temperature, producing the Boltzmann machine - a name designed to emphasize the connection to the statistical physics of Ising spin models." "The Boltzmann machine reduces to the Hopfield model in the special case where the temperature goes to zero. The resulting network, under the Glauber dynamics, produces a homogeneous, irreducible, aperiodic Markov chain as it wanders through state space. The entire theory of Markov chains becomes applicable to the Boltzmann machine." "With ten chapters, five appendices, a list of references, and an index, this study should serve as an introduction to the field of neural networks and its application, and is suitable for an introductory graduate course or an advanced undergraduate course."--BOOK JACKET.Title Summary field provided by Blackwell North America, Inc. All Rights Reserved
Artificial Neural Networks And Machine Learning Icann 2012
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Author : Alessandro Villa
language : en
Publisher: Springer
Release Date : 2012-09-19
Artificial Neural Networks And Machine Learning Icann 2012 written by Alessandro Villa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-19 with Computers categories.
The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.
Efficient Processing Of Deep Neural Networks
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Author : Vivienne Sze
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Efficient Processing Of Deep Neural Networks written by Vivienne Sze and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Computation And Neural Systems
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Author : Frank H. Eeckman
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Computation And Neural Systems written by Frank H. Eeckman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Computational neuroscience is best defined by its focus on understanding the nervous systems as a computational device rather than by a particular experimental technique. Accordinlgy, while the majority of the papers in this book describe analysis and modeling efforts, other papers describe the results of new biological experiments explicitly placed in the context of computational issues. The distribution of subjects in Computation and Neural Systems reflects the current state of the field. In addition to the scientific results presented here, numerous papers also describe the ongoing technical developments that are critical for the continued growth of computational neuroscience. Computation and Neural Systems includes papers presented at the First Annual Computation and Neural Systems meeting held in San Francisco, CA, July 26--29, 1992.
Computational Neuroscience
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Author : Jianfeng Feng
language : en
Publisher: CRC Press
Release Date : 2003-10-20
Computational Neuroscience written by Jianfeng Feng and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-10-20 with Mathematics categories.
How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues to evolve, we find that it provides a theoretical foundation and a set of technological approaches that can significantly enhance our understanding.