Pulsed Neural Networks

Overview

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. 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.

Table of Contents

  1. Foreword

    Terrence J. Sejnowski

  2. Preface
  3. Contributors
  4. 1. Spiking Neurons

    Wulfram Gerstner

  5. 2. Computing with Spiking Neurons

    Wolfgang Maass

  6. 3. Pulse-Based Computation in VLSI Neural Networks

    Alan F. Murray

  7. 4. Encoding Information in Neuronal Activity

    Michael Recce

  8. 5. Building Silicon Nervous Systems with Dendritic Tree Neuromorphs

    John G. Elias and David P. M. Northmore

  9. 6. A Pulse-Coded Communications Infrastructure for Neuromorphic Systems

    Stephen R. Deiss, Rodney J. Douglas and Adrian M. Whatley

  10. 7. Analog VLSI Pulsed Networks for Perceptive Processing

    Alessandro Mortara and Philippe Venier

  11. 8. Preprocessing for Pulsed Neural VLSI Syste

    Alister Hamilton and Kostas A. Papathanasiou

  12. 9. Digital Simulation of Spiking Neural Networks

    Axel Jahnke, Ulrich Roth and Tim Schönauer

  13. 10. Populations of Spiking Neurons

    Wulfram Gerstner

  14. 11. Collective Excitation Phenomena and Their Applications

    David Horn and Irit Opher

  15. 12. Computing and Learning with Dynamic Synapses

    Wolfgang Maass and Anthony M. Zador

  16. 13. Stochastic Bit-Stream Neural Networks

    Peter S. Burge, Max R. van Daalen, Barry J. P. Rising and John S. Shawe-Taylor

  17. 14. Hebbian Learning of Pulse Timing in the Barn Owl Auditory System

    Wulfram Gerstner, Richard Kempter, J. Leo van Hemmen and Hermann Wagner