Ultra low-power integrated circuit design for wireless neural interfaces

✍ Scribed by Holleman, Jeremy;Zhang, Fan;Otis, Brian

• Publisher: Springer
• Year: 2010;2011
• Tongue: English
• Leaves: 123
• Category: Library

✦ Synopsis

This book will describe 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 will describe 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.

✦ Table of Contents

Analog Signal Path......Page 4
Cover......Page 1
Results......Page 3
Contents......Page 5
Spike Detector......Page 6
Decision Circuit......Page 9
Conversion and Control......Page 2
Test Results......Page 7
Introduction......Page 8
Experimental Results......Page 11
Memory Cell Retention......Page 13
Classification......Page 14
Clustering Convergence......Page 16
Thermal Noise......Page 19
Conclusions......Page 12
Characteristics of the Recording Electrodes......Page 15
Brain Recordings......Page 17
Flicker Noise, 1/f Noise......Page 18
Open-Loop Amplifier Design......Page 21
Results......Page 23
Effect of Non-Linearity on Neural Recordings......Page 26
Conclusions......Page 29
Closed-Loop Architecture......Page 31
Analysis of Pseudo-Resistors......Page 32
Telescopic OTA Design Overview......Page 33
Design Optimization......Page 34
Stability and Common-Mode Feedback......Page 35
Design of an Closed-Loop Fully-Differential Complementary-Input Amplifier......Page 36
Design of a Variable-Gain Amplifier......Page 39
Amplifier Testing......Page 42
Variable Gain Amplifier (VGA) Testing......Page 44
In-Vivo Testing......Page 46
Open-Loop Operation Principle......Page 50
Closed-Loop Operation Principle......Page 51
Transfer Function......Page 53
Amplifier Noise......Page 54
The Spike Detection Task......Page 55
Spike Detection Techniques......Page 57
Analog and Mixed-Mode Computation......Page 58
System Design......Page 59
Spike Detector......Page 60
Feature Extraction......Page 61
Analog-Digital Converter......Page 62
Results......Page 63
Overview......Page 68
K-Means Clustering Algorithm......Page 70
On-Line Median Learning......Page 72
Non-Ideal Computational Elements......Page 74
Asymmetric Updates......Page 75
Floating-Gate Memories......Page 78
Device Characterization......Page 79
Clustering Circuit......Page 82
Floating-Gate Memory Cell......Page 84
Decision Circuit......Page 86
Update Rates......Page 88
Memory Cell Retention......Page 90
Classification......Page 91
Clustering Convergence......Page 93
Discussion......Page 96
Previous Neural Recording Systems......Page 100
System Design......Page 102
Analog Signal Path......Page 103
Digital Control......Page 106
Experimental Results......Page 110
Conclusions......Page 111
Analog Front End......Page 114
Conversion and Control......Page 115
MICS-band Wireless Transmitter......Page 116
Conclusions......Page 119
Index......Page 121