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Event-Based Neuromorphic Systems.
Neuromorphic electronic engineering takes its inspiration from the functioning of nervous systems to build more power efficient electronic sensors and processors. Event-based neuromorphic systems are inspired by the brain's efficient data-driven communication design, which is key to its quick r...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Wiley,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- EVENT-BASED NEUROMORPHIC SYSTEMS; Contents; List of Contributors; Foreword; Acknowledgments; List of Abbreviations and Acronyms; 1 Introduction; 1.1 Origins and Historical Context; 1.2 Building Useful Neuromorphic Systems; References; Part I Understanding Neuromorphic Systems; 2 Communication; 2.1 Introduction; 2.2 Address-Event Representation; 2.2.1 AER Encoders; 2.2.2 Arbitration Mechanisms; 2.2.3 Encoding Mechanisms; 2.2.4 Multiple AER Endpoints; 2.2.5 Address Mapping; 2.2.6 Routing; 2.3 Considerations for AER Link Design; 2.3.1 Trade-off: Dynamic or Static Allocation.
- 2.3.2 Trade-off: Arbitered Access or Collisions?2.3.3 Trade-off: Queueing versus Dropping Spikes; 2.3.4 Predicting Throughput Requirements; 2.3.5 Design Trade-offs; 2.4 The Evolution of AER Links; 2.4.1 Single Sender, Single Receiver; 2.4.2 Multiple Senders, Multiple Receivers; 2.4.3 Parallel Signal Protocol; 2.4.4 Word-Serial Addressing; 2.4.5 Serial Differential Signaling; 2.5 Discussion; References; 3 Silicon Retinas; 3.1 Introduction; 3.2 Biological Retinas; 3.3 Silicon Retinas with Serial Analog Output; 3.4 Asynchronous Event-Based Pixel Output Versus Synchronous Frames; 3.5 AER Retinas.
- 3.5.1 Dynamic Vision Sensor3.5.2 Asynchronous Time-Based Image Sensor; 3.5.3 Asynchronous Parvo-Magno Retina Model; 3.5.4 Event-Based Intensity-Coding Imagers (Octopus and TTFS); 3.5.5 Spatial Contrast and Orientation Vision Sensor (VISe); 3.6 Silicon Retina Pixels; 3.6.1 DVS Pixel; 3.6.2 ATIS Pixel; 3.6.3 VISe Pixel; 3.6.4 Octopus Pixel; 3.7 New Specifications for Silicon Retinas; 3.7.1 DVS Response Uniformity; 3.7.2 DVS Background Activity; 3.7.3 DVS Dynamic Range; 3.7.4 DVS Latency and Jitter; 3.8 Discussion; References; 4 Silicon Cochleas; 4.1 Introduction; 4.2 Cochlea Architectures.
- 4.2.1 Cascaded 1D4.2.2 Basic 1D Silicon Cochlea; 4.2.3 2D Architecture; 4.2.4 The Resistive (Conductive) Network; 4.2.5 The BM Resonators; 4.2.6 The 2D Silicon Cochlea Model; 4.2.7 Adding the Active Nonlinear Behavior of the OHCs; 4.3 Spike-Based Cochleas; 4.3.1 Q-control of AEREAR2 Filters; 4.3.2 Applications: Spike-Based Auditory Processing; 4.4 Tree Diagram; 4.5 Discussion; References; 5 Locomotion Motor Control; 5.1 Introduction; 5.1.1 Determining Functional Biological Elements; 5.1.2 Rhythmic Motor Patterns; 5.2 Modeling Neural Circuits in Locomotor Control.
- 5.2.1 Describing Locomotor Behavior5.2.2 Fictive Analysis; 5.2.3 Connection Models; 5.2.4 Basic CPG Construction; 5.2.5 Neuromorphic Architectures; 5.3 Neuromorphic CPGs at Work; 5.3.1 A Neuroprosthesis: Control of Locomotion in Vivo; 5.3.2 Walking Robots; 5.3.3 Modeling Intersegmental Coordination; 5.4 Discussion; References; 6 Learning in Neuromorphic Systems; 6.1 Introduction: Synaptic Connections, Memory, and Learning; 6.2 Retaining Memories in Neuromorphic Hardware; 6.2.1 The Problem of Memory Maintenance: Intuition; 6.2.2 The Problem of Memory Maintenance: Quantitative Analysis.