Note: Intro -- Half Title page -- Title page -- Copyright page -- Preface -- Acknowledgements -- Chapter 1: Basic Ideas, Interpretation Issues and Modeling Hierarchies -- 1.1 Background and Approaches -- 1.2 Modeling Hierarchies -- 1.3 Experimental Methods and Tool Calibration -- 1.4 References -- Chapter 2: Single-Phase Flow Forward and Inverse Algorithms -- 2.1 Overview -- 2.2 Basic Model Summaries -- Chapter 3: Advanced Drawdown and Buildup Interpretation in Low Mobility Environments -- 3.1 Basic Steady Flow Model -- 3.2 Transient Spherical Flow Models -- 3.3 Multiple-Drawdown Pressure Analysis (Patent Pending) -- 3.4 Forward Analysis with Illustrative Calibration -- 3.5 Mobility and Pore Pressure Using First Drawdown Data -- 3.6 Mobility and Pore Pressure from Last Buildup Data -- 3.7 Tool Calibration in Low Mobility Applications -- 3.8 Closing Remarks -- 3.9 References -- Chapter 4: Phase Delay and Amplitude Attenuation for Mobility Prediction in Anisotropic Media with Dip* -- 4.1 Basic Mathematical Results -- 4.2 Numerical Examples and Typical Results -- 4.3 Layered Model Formulation -- 4.4 Phase Delay Software Interface -- 4.5 Detailed Phase Delay Results in Layered Anisotropic Media -- 4.6 Typical Experimental Results -- 4.7 Closing Remarks - Extensions and Additional Applications -- 4.8 References -- Chapter 5: Four Permeability Prediction Methods -- 5.1 Steady-State Drawdown Example -- 5.2 Early-Time, Low-Mobility Drawdown-Buildup -- 5.3 Early-Time, Low-Mobility Drawdown Approach -- 5.4 Phase Delay, Non-Ideal Rectangular Flow Excitation -- Chapter 6: Multiphase Flow with Inertial Effects -- 6.1 Physical Problem Description -- 6.2 Immiscible Flow Formulation -- 6.3 Miscible Flow Formulation -- 6.4 Inertial Effects with Forchheimer Corrections -- 6.5 References -- Chapter 7: Multiphase Flow - Miscible Mixing Clean-Up Examples. , 7.1 Overview Capabilities -- 7.2 Source Code and User Interface Improvements -- 7.3 Detailed Applications -- Chapter 8: Time-Varying Flowline Volume -- 8.1 Transient Anisotropic Formulation for Ellipsoidal Source -- 8.2 FT-06 Software Interface and Example Calculations -- 8.3 Time-Varying Flowline Volume Model -- Chapter 9: Closing Remarks -- References -- Index -- About the Authors.

Note: Intro -- Preface -- Contents -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Spiking Neurons -- 1.2.1 Biological Background -- 1.2.2 Generations of Neuron Models -- 1.2.3 Spiking Neuron Models -- 1.3 Neural Codes -- 1.3.1 Rate Code -- 1.3.2 Temporal Code -- 1.3.3 Temporal Code Versus Rate Code -- 1.4 Cognitive Learning and Memory in the Brain -- 1.4.1 Temporal Learning -- 1.4.2 Cognitive Memory in the Brain -- 1.5 Objectives and Contributions -- 1.6 Outline of the Book -- References -- 2 Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons -- 2.1 Introduction -- 2.2 The Spiking Neural Network -- 2.3 Single-Spike Temporal Coding -- 2.4 Temporal Learning Rule -- 2.4.1 The Tempotron Rule -- 2.4.2 The ReSuMe Rule -- 2.4.3 The Tempotron-Like ReSuMe Rule -- 2.5 Simulation Results -- 2.5.1 The Data Set and the Classification Problem -- 2.5.2 Encoding Images -- 2.5.3 Choosing Among Temporal Learning Rules -- 2.5.4 The Properties of Tempotron Rule -- 2.5.5 Recognition Performance -- 2.6 Discussion -- 2.6.1 Encoding Benefits from Biology -- 2.6.2 Types of Synapses -- 2.6.3 Schemes of Readout -- 2.6.4 Extension of the Network for Robust Sound Recognition -- 2.7 Conclusion -- References -- 3 A Spike-Timing Based Integrated Model for Pattern Recognition -- 3.1 Introduction -- 3.2 The Integrated Model -- 3.2.1 Neuron Model and General Structure -- 3.2.2 Latency-Phase Encoding -- 3.2.3 Supervised Spike-Timing Based Learning -- 3.3 Numerical Simulations -- 3.3.1 Network Architecture and Encoding of Grayscale Images -- 3.3.2 Learning Performance -- 3.3.3 Generalization Capability -- 3.3.4 Parameters Evaluation -- 3.3.5 Capacity of the Integrated System -- 3.4 Related Works -- 3.5 Conclusions -- References -- 4 Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns. , 4.1 Introduction -- 4.2 Methods -- 4.2.1 Spiking Neuron Model -- 4.2.2 PSD Learning Rule -- 4.3 Results -- 4.3.1 Association of Single-Spike and Multi-spike Patterns -- 4.3.2 Generality to Different Neuron Models -- 4.3.3 Robustness to Noise -- 4.3.4 Learning Capacity -- 4.3.5 Effects of Learning Parameters -- 4.3.6 Classification of Spatiotemporal Patterns -- 4.4 Discussion and Conclusion -- References -- 5 A Spiking Neural Network System for Robust Sequence Recognition -- 5.1 Introduction -- 5.2 The Integrated Network for Sequence Recognition -- 5.2.1 Rationale of the Whole System -- 5.2.2 Neural Encoding Method -- 5.2.3 Item Recognition with the PSD Rule -- 5.2.4 The Spike Sequence Decoding Method -- 5.3 Experimental Results -- 5.3.1 Learning Performance Analysis of the PSD Rule -- 5.3.2 Item Recognition -- 5.3.3 Spike Sequence Decoding -- 5.3.4 Sequence Recognition System -- 5.4 Discussions -- 5.4.1 Temporal Learning Rules and Spiking Neurons -- 5.4.2 Spike Sequence Decoding Network -- 5.4.3 Potential Applications in Authentication -- 5.5 Conclusion -- References -- 6 Temporal Learning in Multilayer Spiking Neural Networks Through Construction of Causal Connections -- 6.1 Introduction -- 6.2 Multilayer Learning Rules -- 6.2.1 Spiking Neuron Model -- 6.2.2 Multilayer PSD Rule -- 6.2.3 Multilayer Tempotron Rule -- 6.3 Heuristic Discussion on the Multilayer Learning Rules -- 6.4 Simulation Results -- 6.4.1 Construction of Causal Connections -- 6.4.2 The XOR Benchmark -- 6.4.3 The Iris Benchmark -- 6.5 Discussion and Conclusion -- References -- 7 A Hierarchically Organized Memory Model with Temporal Population Coding -- 7.1 Introduction -- 7.2 The Hierarchical Organized Memory Model -- 7.2.1 Neuron Models and Neural Oscillations -- 7.2.2 Temporal Population Coding -- 7.2.3 The Tempotron Learning and STDP -- 7.3 Simulation Results. , 7.3.1 Auto-Associative Memory -- 7.3.2 Episodic Memory -- 7.4 Discussion -- 7.4.1 Information Flow and Emergence of Neural Cliques -- 7.4.2 Storage, Recall and Organization of Memory -- 7.4.3 Temporal Compression and Information Binding -- 7.4.4 Related Works -- 7.5 Conclusion -- References -- 8 Spiking Neuron Based Cognitive Memory Model -- 8.1 Introduction -- 8.2 SRM-Based CA3 Model -- 8.2.1 Spike Response Model -- 8.2.2 SRM-Based Pyramidal Cell -- 8.2.3 SRM-Based Interneuron -- 8.3 Convergence of Synaptic Weight -- 8.4 Maximum Synaptic Weight to Prevent Early Activation -- 8.5 Pattern Completion of Auto-Associative Memory -- 8.6 Discussion -- 8.7 Conclusion -- References.