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Ultra Low-Power Integrated Circuit Design for Wireless Neural Interfaces [electronic resource] / by Jeremy Holleman, Fan Zhang, Brian Otis.

By: Holleman, Jeremy [author.].
Contributor(s): Zhang, Fan | [author.] | Otis, Brian | [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookDescription: VII, 121p. 100 illus. online resource.ISBN: 9781441967275 99781441967275.Subject(s): Engineering | Engineering | COMPUTER, AIDED ENGINEERING (CAD, CAE) AND DESIGN | COMPUTER AIDED DESIGN | CIRCUITS AND SYSTEMS | SYSTEMS ENGINEERINGDDC classification: 621.3815 Online resources: ir a documento Summary: Micro-power Integrated Circuits for Neural Interfaces Jeremy Holleman Fan Zhang Brian Otis This book describes ultra low-power, integrated circuits and systems designed for the emerging field of neural signal recording and processing, and wireless communication. Since neural interfaces are typically implanted, their operation is highly energy-constrained. This book introduces concepts and theory that allow circuit operation approaching the fundamental limits. Design examples and measurements of real systems are provided. The book describes circuit designs for all of the critical components of a neural recording system, including: - Amplifiers which utilize new techniques to improve the trade-off between good noise performance and low power consumption - Analog and mixed-signal circuits which implement signal processing tasks specific to the neural recording application: - Detection of neural spikes - Extraction of features that describe the spikes - Clustering, a machine learning technique for sorting spikes - Weak-inversion operation of analog-domain transistors, allowing processing circuits that reduce the requirements for analog-digital conversion and allow low system-level power consumption. - Highly-integrated, sub-mW wireless transmitter designed for the Medical Implant Communications Service (MICS) and ISM bands. Covers analog, radio, and signal processing theory and design for ultra low-power circuits, tied together throughout the book to form an entire system; Provides fundamental requirements of neural recording systems, as well as design techniques for reducing power consumption in low-noise amplifiers; Includes miniaturized system-integration techniques in the context of ultra low-power circuits; Uses results from real prototype systems to demonstrate techniques described
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DOCUMENTOS DIGITALES DOCUMENTOS DIGITALES Biblioteca Jorge Álvarez Lleras
Digital 621.3815 223 (Browse shelf) Ej. 1 1 Available D000760
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Micro-power Integrated Circuits for Neural Interfaces Jeremy Holleman Fan Zhang Brian Otis This book describes ultra low-power, integrated circuits and systems designed for the emerging field of neural signal recording and processing, and wireless communication. Since neural interfaces are typically implanted, their operation is highly energy-constrained. This book introduces concepts and theory that allow circuit operation approaching the fundamental limits. Design examples and measurements of real systems are provided. The book describes circuit designs for all of the critical components of a neural recording system, including: - Amplifiers which utilize new techniques to improve the trade-off between good noise performance and low power consumption - Analog and mixed-signal circuits which implement signal processing tasks specific to the neural recording application: - Detection of neural spikes - Extraction of features that describe the spikes - Clustering, a machine learning technique for sorting spikes - Weak-inversion operation of analog-domain transistors, allowing processing circuits that reduce the requirements for analog-digital conversion and allow low system-level power consumption. - Highly-integrated, sub-mW wireless transmitter designed for the Medical Implant Communications Service (MICS) and ISM bands. Covers analog, radio, and signal processing theory and design for ultra low-power circuits, tied together throughout the book to form an entire system; Provides fundamental requirements of neural recording systems, as well as design techniques for reducing power consumption in low-noise amplifiers; Includes miniaturized system-integration techniques in the context of ultra low-power circuits; Uses results from real prototype systems to demonstrate techniques described

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