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    Pulsed Neural Networks (Bradford Books) (Hardback) Edited by Wolfgang Maass, Edited by Christopher M. Bishop, Foreword by Terrence J. Sejnowski

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    DescriptionMost practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. In recent years, data from neurobiological experiments have made it increasingly clear that biological neural networks, which communicate through pulses, use the timing of the pulses to transmit information and perform computation. This realization has stimulated significant research on pulsed neural networks, including theoretical analyses and model development, neurobiological modeling, and hardware implementation. This book presents the complete spectrum of current research in pulsed neural networks and includes the most important work from many of the key scientists in the field. Terrence J. Sejnowski's foreword, "Neural Pulse Coding," presents an overview of the topic. The first half of the book consists of longer tutorial articles spanning neurobiology, theory, algorithms, and hardware. The second half contains a larger number of shorter research chapters that present more advanced concepts. The contributors use consistent notation and terminology throughout the book. Contributors: Peter S. Burge, Stephen R. Deiss, Rodney J. Douglas, John G. Elias, Wulfram Gerstner, Alister Hamilton, David Horn, Axel Jahnke, Richard Kempter, Wolfgang Maass, Alessandro Mortara, Alan F. Murray, David P. M. Northmore, Irit Opher, Kostas A. Papathanasiou, Michael Recce, Barry J. P. Rising, Ulrich Roth, Tim Schonauer, Terrence J. Sejnowski, John Shawe-Taylor, Max R. van Daalen, J. Leo van Hemmen, Philippe Venier, Hermann Wagner, Adrian M. Whatley, Anthony M. Zador.

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  • Full bibliographic data for Pulsed Neural Networks

    Pulsed Neural Networks
    Authors and contributors
    Edited by Wolfgang Maass, Edited by Christopher M. Bishop, Foreword by Terrence J. Sejnowski
    Physical properties
    Format: Hardback
    Number of pages: 377
    Width: 178 mm
    Height: 254 mm
    Thickness: 22 mm
    Weight: 953 g
    ISBN 13: 9780262133500
    ISBN 10: 0262133504

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    B&T Modifier: Text Format: 02
    Warengruppen-Systematik des deutschen Buchhandels: 16320
    B&T Modifier: Academic Level: 02
    B&T General Subject: 229
    LC subject heading:
    B&T Merchandise Category: COM
    BIC subject category V2: UYQN
    BISAC V2.8: COM014000, COM043000
    Ingram Subject Code: XE
    Libri: I-XE
    DC21: 006.32
    DC22: 006.32
    B&T Approval Code: A93203605
    BISAC V2.8: COM044000
    DC22: 006.3/2
    LC classification: QA76.87 .P85 1999
    Thema V1.0: UT, UYQN
    Edition statement
    New ed.
    Illustrations note
    MIT Press Ltd
    Imprint name
    MIT Press
    Publication date
    29 January 1999
    Publication City/Country
    Cambridge, Mass.
    Author Information
    Wolfgang Maass is Professor at the Institute for Theoretical ComputerScience, Technische Universitat Graz. Christopher M. Bishop isAssistant Director at Microsoft Research, Cambridge, and Professor ofComputer Science at the University of Edinburgh.
    Review quote
    " Pulsed Neural Networks is a welcome new breeze in the field ofneuronal modeling. At last, the central issue of timing in neuronalnetwork function is treated in its full depth--a must for anyoneseriously interested in CNS function." Rodolfo Llinas, Department of Physiology and Neuroscience, New York University Medical School
    Table of contents
    Basic concepts and models. Part 1 Spiking neurons: the problem of neural coding; neuron models; conclusions. Part 2 Computing with Spiking neurons: introduction; a formal computational model for a network of Spiking neurons; McCullogh-Pitts neurons versus Spiking neurons; computing with temporal patterns; computing with a space-rate code; computing with firing rates; firing rates and temporal correlations; networks of Spiking neurons for storing and retrieving information; computing on Spike trains; conclusions. Part 3 Pulse-based computation in VLSI neural networks: background; pulsed coding - a VLSI perspective; a MOSFET introduction; pulse generation VLSI; pulsed arithmetic in VLSI; learning in pulsed systems; summary and issues raised. Part 4 Encoding information in neuronal activity: introduction; synchronization and oscillations; temporal binding; phase coding; dynamic range and firing rate codes; interspike interval variability; synapses and rate coding; summary and implications. Part 5 Building silicon nervous systems with dendritic tree neuromorphs: introduction; implementation in VLSI; neuromorphs in action; conclusions. Part 6 A pulse-coded communications infrastructure: introduction; neuromorphic computational nodes; neuromorphic VLSI neurons; address event representation (AER); implementations of AER; silicon cortex; functional tests of silicon cortex; future research on AER neuromorphic systems. Part 7 Analog VLSI pulsed networks for perceptive processing: introduction; analog perceptive nets communication requirements; analysis of the NAPFM communication system; address coding; silicon retina equipped with the NAPFM communication system; projective field generation; description of the integrated circuit for orientation enhancement; display interface; conclusion. Part 8 Preprocessing for pulsed neural VLSI systems: introduction; a sound segmentation system; signal processing in analog VLSI; Palmo - pulse based signal processing; conclusions; further works. Part 9 Digital simulation of Spiking neural networks: introduction; implementation issues of pulse-coded neural networks; programming environment; concepts of efficient simulation; mapping neural networks on parallel computers; performance study. Part 10 Populations of Spiking neurons: introduction; model; population activity equation; noise-free population dynamics; locking; transients; incoherent firing; conclusions. Part 11 Collective excitation phenomena and their applications: introduction; synchronization of pulse coupled oscillators; clustering via temporal segmentation; limits on temporal segmentation; image analysis; solitary waves; the importance of noise; conclusions. Part 12 Computing and learning with dynamic synapses: introduction; biological data on dynamic synapses; quantitative models; on the computational role of dynamic synapses; implications for learning in pulsed neural nets; conclusions. Part 13 Stochastic bit-stream neural networks: introduction; basic neur