Pulsed Neural Networks

Pulsed Neural Networks

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MIT Press

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Pulsed Neural Networks

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.

Product Details

ISBN-13: 9780262632218
Publisher: MIT Press
Publication date: 01/26/2001
Series: Bradford Books Series
Edition description: New Edition
Pages: 377
Product dimensions: 7.00(w) x 10.00(h) x 0.90(d)

Table of Contents

Foreword by Terrence J. Sejnowski
Contributors to the book
Basic Concepts and Models
1 Spiking Neurons
1.1 The Problem of Neural Coding
1.2 Neuron Models
1.3 Conclusions
2 Computing with Spiking Neurons
2.1 Introduction
2.2 A Formal Computational Model for a Network of Spiking Neurons
2.3 McCullochPitts Neurons versus Spiking Neurons
2.4 Computing with Temporal Patterns
2.5 Computing with a SpaceRate Code
2.6 Computing with Firing Rates
2.7 Firing Rates and Temporal Correlations
2.8 Networks of Spiking Neurons for Storing and Retrieving
2.9 Computing on Spike Trains
2.10 Conclusions
3 PulseBased Computation in VLSI Neural Networks
3.1 Background
3.2 Pulsed Coding: A VLSI Perspective
3.3 A MOSFET Introduction
3.4 Pulse Generation VLSI
3.5 Pulsed Arithmetic in VLSI
3.6 Learning in Pulsed Systems
3.7 Summary and Issues Raised
4 Encoding Information in Neuronal Activity
4.1 Introduction
4.2 Synchronization and Oscillations
4.3 Temporal Binding
4.4 Phase Coding
4.5 Dynamic Range and Firing Rate Codes
4.6 Interspike Interval Variability
4.7 Synapses and Rate Coding
4.8 Summary and Implications
5 Building Silicon Nervous Systems with Dendritic Tree
5.1 Introduction
5.2 Implementation in VLSI
5.3 Neuromorphs in Action
5.4 Conclusions
6 A PulseCoded Communications Infrastructure
6.1 Introduction
6.2 Neuromorphic Computational Nodes
6.3 Neuromorphic aVLSI Neurons
6.4 Address Event Representation (AER)
6.5 Implementations of AER
6.6 SiliconCorteX
6.7 Functional Tests of Silicon CorteX
6.8 Future Research on AER Neuromorphic Systems
7 Analog VLSI Pulsed Networks for Perceptive
7.1 Introduction
7.2 Analog Perceptive Nets Communication Requirements
7.3 Analysis of the NAPFM Communication System
7.4 Address Coding
7.5 Silicon Retina Equipped with the NAPFM Commuincation System
7.6 Projective Field Generation
7.7 Description of the Integrated Circuit for Orientation
7.8 Display Interface
7.9 Conclusion
8 Preprocessing for Pulsed Neural VLSI Systems
8.1 Introduction
8.2 A Sound Segmentation System
8.3 Signal Processing in Analog VLSI
8.4 Palmo Pulse Based Signal Processing
8.5 Conclusions
8.6 Further Work
8.7 Acknowledgements
9 Digital Simulation of Spiking Neural Networks
9.1 Introduction
9.2 Implementation Issues of PulseCoded Neural Networks
9.3 Programming Environment
9.4 Concepts of efficient Simulation
9.5 Mapping Neural Networks on Parallel Computers
9.6 Performance Study
Design and Analysis of Pulsed Neural Systems
10 Populations of Spiking Neurons
10.1 Introduction
10.2 Model
10.3 Population Activity Equation
10.4 NoiseFree Population Dynamics
10.5 Locking
10.6 Transients
10.7 Incoherent Firing
10.8 Conclusions
11 Collective EXcitation Phenomena and Their
11.1 Introduction
11.2 Synchronization of Pulse Coupled Oscillators
11.3 Clustering via Temporal Segmentation
11.4 Limits on Temporal Segmentation
11.5 Image Analysis
11.6 Solitary Waves
11.7 The Importance of Noise
11.8 Conclusions
12 Computing and Learning with Dynamic Synapses
12.1 Introduction
12.2 Biological Data on Dynamic Synapses
12.3 Quantitative Models
12.4 On the Computational Role of Dynamic Synapses
12.5 Implications for Learning in Pulsed Neural Nets
12.6 Conclusions
13 Stochastic BitStream Neural Networks
13.1 Introduction
13.2 Basic Neural Modelling
13.3 Feedforward Networks and Learning
13.4 Generalization Analysis
13.5 Recurrent Networks
13.6 Applications to Graph Colouring
13.7 Hardware Implementation
13.8 Conclusions
14 Hebbian Learning of Pulse Timing in the Barn
Auditory System
14.1 Introduction
14.2 Hebbian Learning
14.3 Barn Owl Auditory System
14.4 Phase Locking
14.5 Delay Tuning by Hebbian Learning
14.6 Conclusions

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