Journal Article10.1109/TBCAS.2007.907868
An Energy-Efficient Micropower Neural Recording Amplifier
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TL;DR: The amplifier appears to be the lowest power and most energy-efficient neural recording amplifier reported to date and the low-noise design techniques that help the neural amplifier achieve input-referred noise that is near the theoretical limit of any amplifier using a differential pair as an input stage.
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Abstract: This paper describes an ultralow-power neural recording amplifier. The amplifier appears to be the lowest power and most energy-efficient neural recording amplifier reported to date. We describe low-noise design techniques that help the neural amplifier achieve input-referred noise that is near the theoretical limit of any amplifier using a differential pair as an input stage. Since neural amplifiers must include differential input pairs in practice to allow robust rejection of common-mode and power supply noise, our design appears to be near the optimum allowed by theory. The bandwidth of the amplifier can be adjusted for recording either neural spikes or local field potentials (LFPs). When configured for recording neural spikes, the amplifier yielded a midband gain of 40.8 dB and a -3-dB bandwidth from 45 Hz to 5.32 kHz; the amplifier's input-referred noise was measured to be 3.06 muVrms while consuming 7.56 muW of power from a 2.8-V supply corresponding to a noise efficiency factor (NEF) of 2.67 with the theoretical limit being 2.02. When configured for recording LFPs, the amplifier achieved a midband gain of 40.9 dB and a -3-dB bandwidth from 392 mHz to 295 Hz; the input-referred noise was 1.66 muVrms while consuming 2.08 muW from a 2.8-V supply corresponding to an NEF of 3.21. The amplifier was fabricated in AMI's 0.5-mum CMOS process and occupies 0.16 mm2 of chip area. We obtained successful recordings of action potentials from the robust nucleus of the arcopallium (RA) of an anesthesized zebra finch brain with the amplifier. Our experimental measurements of the amplifier's performance including its noise were in good accord with theory and circuit simulations.
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Citations
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References
•Book
Operation and modeling of the MOS transistor
Yannis Tsividis
- 01 Jan 1987
TL;DR: In this article, the MOS transistors with ION-IMPLANTED CHANNELS were used for CIRCUIT SIMULATION in a two-and three-tier MOS structure.
3.6K
A low-power low-noise CMOS amplifier for neural recording applications
R.R. Harrison,C.T. Charles +1 more
TL;DR: In this article, a low-noise low-power biosignal amplifiers capable of amplifying signals in the millihertz-to-kilohertz range while rejecting large dc offsets generated at the electrode-tissue interface is presented.
1.7K
Real-time prediction of hand trajectory by ensembles of cortical neurons in primates
Johan Wessberg,Christopher R. Stambaugh,Jerald D. Kralik,Pamela D. Beck,Mark Laubach,John K. Chapin,Jung Kim,James Biggs,Mandayam A. Srinivasan,Miguel A. L. Nicolelis +9 more
TL;DR: The results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.
1.5K
An analytical MOS transistor model valid in all regions of operation and dedicated to low-voltage and low-current applications
TL;DR: In this article, a fully analytical MOS transistor model dedicated to the design and analysis of low-voltage, low-current analog circuits is presented, which exploits the inherent symmetry of the device by referring all the voltages to the local substrate.
1.3K
Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex
TL;DR: A possible means for movement restoration in paralysis patients is suggested after rats trained to position a robot arm to obtain water by pressing a lever routinely used brain-derived signals to position the robot arm and obtain water.