| 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： |
ＥＢ
図書
