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1.

図書

図書
International Conference on Artificial Neural Networks ; Institution of Electrical Engineers
出版情報: London : Institution of Electrical Engineers, 1999  2v.(xxix,1028p.) ; 30cm
シリーズ名: IEE conference publication
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2.

図書

図書
edited by Lakhmi C. Jain, V. Rao Vemuri
出版情報: Boca Raton, Fla. : CRC Press, c1999  325 p. ; 25 cm
シリーズ名: The CRC Press international series on computational intelligence / series editor L. C. Jain
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3.

図書

図書
edited by Wolfgang Maass, Christopher M. Bishop
出版情報: Cambridge, Mass. : MIT Press, c1999  xxix, 377 p. ; 26 cm
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目次情報: 続きを見る
Foreword
Neural Pulse Coding
Spike Timing
Population Codes
Hippocampal Place Field
Hardware Models
References
Preface
The Isaac Newton Institute
Overview of the Book
Acknowledgments
Contributors
Basic Concepts and Models / Part I:
Spiking Neurons / 1:
The Problem of Neural Coding / 1.1:
Motivation / 1.1.1:
Rate Codes / 1.1.2:
Rate as a Spike Count (Average over Time) / 1.1.2.1:
Rate as a Spike Density (Average over Several Runs) / 1.1.2.2:
Rate as Population Activity (Average over Several Neurons) / 1.1.2.3:
Candidate Pulse Codes / 1.1.3:
Time-to-First-Spike / 1.1.3.1:
Phase / 1.1.3.2:
Correlations and Synchrony / 1.1.3.3:
Stimulus Reconstruction and Reverse Correlation / 1.1.3.4:
Discussion: Spikes or Rates? / 1.1.4:
Neuron Models / 1.2:
Simple Spiking Neuron Model / 1.2.1:
First Steps towards Coding by Spikes / 1.2.2:
Threshold-Fire Models / 1.2.3:
Spike Response Model -- Further Details / 1.2.3.1:
Integrate-and-Fire Model / 1.2.3.2:
Models of Noise / 1.2.3.3:
Conductance-Based Models / 1.2.4:
Hodgkin-Huxley Model / 1.2.4.1:
Relation to the Spike Response Model / 1.2.4.2:
Compartmental Models / 1.2.4.3:
Rate Models / 1.2.5:
Conclusions / 1.3:
Computing with Spiking Neurons / 2:
Introduction / 2.1:
A Formal Computational Model for a Network of Spiking Neurons / 2.2:
McCulloch-Pitts Neurons versus Spiking Neurons / 2.3:
Computing with Temporal Patterns / 2.4:
Conincidence Detection / 2.4.1:
RBF-Units in the Temporal Domain / 2.4.2:
Computing a Weighted Sum in Temporal Coding / 2.4.3:
Universal Approximation of Continuous Functions with Spiking Neurons Remarks: / 2.4.4:
Other Computations with Temporal Patterns in Networks of Spiking Neurons / 2.4.5:
Computing with a Space-Rate Code / 2.5:
Computing with Firing Rates / 2.6:
Computing with Firing Rates and Temporal Correlations / 2.7:
Networks of Spiking Neurons for Storing and Retrieving Information / 2.8:
Computing on Spike Trains / 2.9:
Pulse-Based Computation in VLSI Neural Networks / 2.10:
Background / 3.1:
Pulsed Coding: A VLSI Perspective / 3.2:
Pulse Amplitude Modulation / 3.2.1:
Pulse Width Modulation / 3.2.2:
Pulse Frequency Modulation / 3.2.3:
Phase or Delay Modulation / 3.2.4:
Noise, Robustness, Accuracy and Speed / 3.2.5:
A MOSFET Introduction / 3.3:
Subthreshold Circuits for Neural Networks / 3.3.1:
Pulse Generation in VLSI / 3.4:
Pulse Intercommunication / 3.4.1:
Pulsed Arithmetic in VLSI / 3.5:
Addition of Pulse Stream Signals / 3.5.1:
Multiplication of Pulse Stream Signals / 3.5.2:
MOS Transconductance Multiplier / 3.5.3:
MOSFET Analog Multiplier / 3.5.4:
Learning in Pulsed Systems / 3.6:
Summary and Issues Raised / 3.7:
Encoding Information in Neuronal Activity / 4:
Synchronization and Oscillations / 4.1:
Temporal Binding / 4.3:
Phase Coding / 4.4:
Dynamic Range and Firing Rate Codes / 4.5:
Interspike Interval Variability / 4.6:
Synapses and Rate Coding / 4.7:
Summary and Implications / 4.8:
Implementations / Part II:
Building Silicon Nervous Systems with Dendritic Tree Neuromorphs / 5:
Why Spikes? / 5.1:
Dendritic Processing of Spikes / 5.1.2:
Tunability / 5.1.3:
Implementation in VLSI / 5.2:
Artificial Dendrites / 5.2.1:
Synapses / 5.2.2:
Dendritic Non-Linearities / 5.2.3:
Spike-Generating Soma / 5.2.4:
Excitability Control / 5.2.5:
Spike Distribution -- Virtual Wires / 5.2.6:
Neuromorphs in Action / 5.3:
Feedback to Threshold-Setting Synapses / 5.3.1:
Discrimination of Complex Spatio-Temporal Patterns / 5.3.2:
Processing of Temporally Encoded Information / 5.3.3:
A Pulse-Coded Communications Infrastructure for Neuromorphic Systems / 5.4:
Neuromorphic Computational Nodes / 6.1:
Neuromorphic aVLSI Neurons / 6.3:
Address Event Representation (AER) / 6.4:
Implementations of AER / 6.5:
Silicon Cortex / 6.6:
Basic Layout / 6.6.1:
Functional Tests of Silicon Cortex / 6.7:
An Example Neuronal Network / 6.7.1:
An Example of Sensory Input to SCX / 6.7.2:
Future Research on AER Neuromorphic Systems / 6.8:
Acknowledgements
Analog VLSI Pulsed Networks for Perceptive Processing / 7:
Analog Perceptive Nets Communication Requirements / 7.1:
Coding Information with Pulses / 7.2.1:
Multiplexing of the Signals Issued by Each Neuron / 7.2.2:
Non-Arbitered PFM Communication / 7.2.3:
Analysis of the NAPFM Communication Systems / 7.3:
Statistical Assumptions / 7.3.1:
Detection / 7.3.2:
Detection by Time-Windowing / 7.3.2.1:
Direct Interpulse Time Measurement / 7.3.2.2:
Performance / 7.3.3:
Data Dependency of System Performance / 7.3.3.1:
Discussion / 7.3.5:
Detection by Direct Interpulse Time Measurement / 7.3.5.1:
Address Coding / 7.4:
Silicon Retina Equipped with the NAPFM Communication System / 7.5:
Circuit Description / 7.5.1:
Noise Measurement Results / 7.5.2:
Projective Field Generation / 7.6:
Overview / 7.6.1:
Anisotropic Current Pulse Spreading in a Nonlinear Network / 7.6.2:
Analysis of the Spatial Response of the Nonlinear Network / 7.6.3:
Analysis of the Size and Shape of the Bubbles Generable by the Nonlinear Network / 7.6.4:
Description of the Integrated Circuit for Orientation Enhancement / 7.7:
System Measurement Results / 7.7.1:
Other Applications / 7.7.4:
Weighted Projective Field Generation / 7.7.4.1:
Complex Projective Field Generation / 7.7.4.2:
Display Interface / 7.8:
Conclusion / 7.9:
Preprocessing for Pulsed Neural VLSI Syste / 8:
A Sound Segmentation System / 8.1:
Signal Processing in Analog VLSI / 8.3:
Continuous Time Active Filters / 8.3.1:
Sampled Data Active Switched Capacitor (SC) Filters / 8.3.2:
Sampled Data Active Switched Current (SI) Filters / 8.3.3:
Palmo -- Pulse Based Signal Processing / 8.3.4:
Basic Palmo Concepts / 8.4.1:
The Palmo Signal Representation / 8.4.1.1:
The Analog Palmo Cell / 8.4.1.2:
A Palmo Signal Processing System / 8.4.1.3:
Sources of Harmonic Distortion in a Palmo System / 8.4.1.4:
A CMOS Analog Palmo Cell Implementation / 8.4.2:
The Analog Palmo Cell: Details of Circuit Operation / 8.4.2.1:
Interconnecting Analog Palmo Cells / 8.4.3:
Results from a Palmo VLSI Device / 8.4.4:
Digital Processing of Palmo Signals / 8.4.5:
CMOS Analog Palmo Cell: Performance / 8.4.6:
Further Work / 8.5:
Digital Simulation of Spiking Neural Networks / 8.7:
Implementation Issues of Pulse-Coded Neural Networks / 9.1:
Discrete-Time Simulation / 9.2.1:
Requisite Arithmetic Precision / 9.2.2:
Basic Procedures of Network Computation / 9.2.3:
Programming Environment / 9.3:
Concepts of Efficient Simulation / 9.4:
Mapping Neural Networks on Parallel Computers / 9.5:
Neuron-Parallelism / 9.5.1:
Synapse-Parallelism / 9.5.2:
Pattern-Parallelism / 9.5.3:
Partitioning of the Network / 9.5.4:
Performance Study / 9.6:
Single PE Workstations / 9.6.1:
Neurocomputer / 9.6.2:
Parallel Computers / 9.6.3:
Results of the Performance Study / 9.6.4:
Design and Analysis of Pulsed Neural Systems / 9.6.5:
Populations of Spiking Neurons / 10:
Model / 10.1:
Population Activity Equation / 10.3:
Integral Equation for the Dynamics / 10.3.1:
Normalization / 10.3.2:
Noise-Free Population Dynamics / 10.4:
Locking / 10.5:
Locking Condition / 10.5.1:
Graphical Interpretation / 10.5.2:
Transients / 10.6:
Incoherent Firing / 10.7:
Determination of the Activity / 10.7.1:
Stability of Asynchronous Firing / 10.7.2:
Collective Excitation Phenomena and Their Applications / 10.8:
Two Variable Formulation of IAF Neurons / 11.1:
Synchronization of Pulse Coupled Oscillators / 11.2:
Clustering via Temporal Segmentation / 11.3:
Limits on Temporal Segmentation / 11.4:
Image Analysis / 11.5:
Image Segmentation / 11.5.1:
Edge Detection / 11.5.2:
Solitary Waves / 11.6:
The Importance of Noise / 11.7:
Acknowledgment / 11.8:
Computing and Learning with Dynamic Synapses / 12:
Biological Data on Dynamic Synapses / 12.1:
Quantitative Models / 12.3:
On the Computational Role of Dynamic Synapses / 12.4:
Implications for Learning in Pulsed Neural Nets / 12.5:
Stochastic Bit-Stream Neural Networks / 12.6:
Basic Neural Modelling / 13.1:
Feedforward Networks and Learning / 13.3:
Probability Level Learning / 13.3.1:
Bit-Stream Level Learning / 13.3.2:
Generalization Analysis / 13.4:
Recurrent Networks / 13.5:
Applications to Graph Colouring / 13.6:
Hardware Implementation / 13.7:
The Stochastic Neuron / 13.7.1:
Calculating Output Derivatives / 13.7.2:
Generating Stochastic Bit-Streams / 13.7.3:
Hebbian Learning of Pulse Timing in the Barn Owl Auditory System / 13.7.4:
Hebbian Learning / 14.1:
Review of Standard Formulations / 14.2.1:
Spike-Based Learning / 14.2.2:
Example / 14.2.3:
Learning Window / 14.2.4:
Barn Owl Auditory System / 14.3:
The Localization Task / 14.3.1:
Auditory Localization Pathway / 14.3.2:
Phase Locking / 14.4:
Neuron Model / 14.4.1:
Phase Locking -- Schematic / 14.4.2:
Simulation Results / 14.4.3:
Delay Tuning by Hebbian Learning / 14.5:
Selection of Delays / 14.5.1:
Foreword
Neural Pulse Coding
Spike Timing
4.

図書

図書
edited by Erkki Oja and Samuel Kaski
出版情報: Amsterdam : Elsevier, 1999  ix, 390 p. ; 25 cm
所蔵情報: loading…
目次情報: 続きを見る
Selected papers only
Preface / Kohonen Maps
Analyzing and representing multidimentional quantitative and qualitative data: Demographic study of the / Rhône valley
The domeatic consumption of the Canadian families / M. Cottrell ; P. Gaubert ; P. Letremy ; P. Rousset
Value maps: Finding value in markets that are expensive / G.J. Deboeck
Data mining and knowledge discovery with emergent Self-Organizing Feature Maps for multivariate time series / A. Ultsch
Tree structured Self-Organizing Maps / P. Koikkalainen
On the optimization of Self-Organizing Maps by genetic algorithms / D. Polani
Self organization of a massive text document collection / T. Kohonen ; S. Kaski ; K. Lagus ; J. Salojárvi ; J. Honkela ; V. Paatero ; A. Saarela
Document classification with Self-Organizing Maps / D. Merkl
Navigation in databases using Self-Organizing Maps / S.A. Shumsky
Self-Organising Maps in computer aided design of electronic circuits / A. Hemani ; A. Postula
Modeling self-organization in the visual cortex / R. Miikkulainen ; J.A. Bednar ; Y. Choe ; J. Sirosh
A spatio-temporal memory based on SOMs with activity diffusion / N.R. Euliano ; J.C. Principe
Advances in modeling cortical maps / P.G. Morasso ; V. Sanguineti ; F. Frisone
Topology preservation in Self-Organizing Maps / T. Villmann
Second-order learing in Self-Organizing Maps / R. Der ; M. Herrmann
Energy functions for Self-Organizing Maps / T. Heskes
LVQ and single trial EEG classification / G. Pfurtscheller ; M. Pregenzer
Self-Organizing Map in categorization of voice qualities / L. Leinonen
Self-Organizing Map in analysis of large-scale industrial systems / O. Simula ; J. Ahola ; E. Alhoniemi ; J. Himberg ; J. Vesanto
Keyword index
Selected papers only
Preface / Kohonen Maps
Analyzing and representing multidimentional quantitative and qualitative data: Demographic study of the / Rhône valley
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