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...
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| Language: | English |
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Hoboken :
Wiley,
2014.
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MARC
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| 100 | 1 | |a Liu, Shih-Chii. | |
| 245 | 1 | 0 | |a Event-Based Neuromorphic Systems. |
| 260 | |a Hoboken : |b Wiley, |c 2014. | ||
| 300 | |a 1 online resource (442 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 500 | |a 6.2.3 Solving the Problem of Memory Maintenance. | ||
| 520 | |a 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 responses and remarkable capabilities. This cross-disciplinary text establishes how circuit building blocks are combined in architectures to construct complete systems. These include vision and auditory sensors as well as neuronal processing and learning circuits that implement models of nervous systems. | ||
| 650 | 0 | |a Neuromorphics. | |
| 650 | 0 | |a Discrete-time systems. | |
| 650 | 0 | |a Neural networks (Neurobiology) |x Simulation methods. | |
| 650 | 7 | |a Discrete-time systems |2 fast | |
| 650 | 7 | |a Neuromorphics |2 fast | |
| 700 | 1 | |a Delbruck, Tobi. | |
| 700 | 1 | |a Indiveri, Giacomo. | |
| 700 | 1 | |a Whatley, Adrian. | |
| 700 | 1 | |a Douglas, Rodney. | |
| 758 | |i has work: |a Event-Based Neuromorphic Systems (Text) |1 https://id.oclc.org/worldcat/entity/E39PD3G8tffMDWgc8gbYkRBQdP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Liu, Shih-Chii. |t Event-Based Neuromorphic Systems. |d Hoboken : Wiley, ©2014 |z 9780470018491 |
| 856 | 4 | 0 | |u https://ebookcentral.proquest.com/lib/ucb/detail.action?docID=1895762 |z Full Text (via ProQuest) |
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