Literature DB >> 23852555

A low-power 32-channel digitally programmable neural recording integrated circuit.

W Wattanapanitch, R Sarpeshkar.   

Abstract

We report the design of an ultra-low-power 32-channel neural-recording integrated circuit (chip) in a 0.18 μ m CMOS technology. The chip consists of eight neural recording modules where each module contains four neural amplifiers, an analog multiplexer, an A/D converter, and a serial programming interface. Each amplifier can be programmed to record either spikes or LFPs with a programmable gain from 49-66 dB. To minimize the total power consumption, an adaptive-biasing scheme is utilized to adjust each amplifier's input-referred noise to suit the background noise at the recording site. The amplifier's input-referred noise can be adjusted from 11.2 μVrms (total power of 5.4 μW) down to 5.4 μVrms (total power of 20 μW) in the spike-recording setting. The ADC in each recording module digitizes the a.c. signal input to each amplifier at 8-bit precision with a sampling rate of 31.25 kS/s per channel, with an average power consumption of 483 nW per channel, and, because of a.c. coupling, allows d.c. operation over a wide dynamic range. It achieves an ENOB of 7.65, resulting in a net efficiency of 77 fJ/State, making it one of the most energy-efficient designs for neural recording applications. The presented chip was successfully tested in an in vivo wireless recording experiment from a behaving primate with an average power dissipation per channel of 10.1 μ W. The neural amplifier and the ADC occupy areas of 0.03 mm(2) and 0.02 mm(2) respectively, making our design simultaneously area efficient and power efficient, thus enabling scaling to high channel-count systems.

Year:  2011        PMID: 23852555     DOI: 10.1109/TBCAS.2011.2163404

Source DB:  PubMed          Journal:  IEEE Trans Biomed Circuits Syst        ISSN: 1932-4545            Impact factor:   3.833


  14 in total

1.  A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.

Authors:  Samuel R Nason; Alex K Vaskov; Matthew S Willsey; Elissa J Welle; Hyochan An; Philip P Vu; Autumn J Bullard; Chrono S Nu; Jonathan C Kao; Krishna V Shenoy; Taekwang Jang; Hun-Seok Kim; David Blaauw; Parag G Patil; Cynthia A Chestek
Journal:  Nat Biomed Eng       Date:  2020-07-27       Impact factor: 25.671

2.  Minimum requirements for accurate and efficient real-time on-chip spike sorting.

Authors:  Joaquin Navajas; Deren Y Barsakcioglu; Amir Eftekhar; Andrew Jackson; Timothy G Constandinou; Rodrigo Quian Quiroga
Journal:  J Neurosci Methods       Date:  2014-04-24       Impact factor: 2.390

3.  A Wide Dynamic Range Neural Data Acquisition System With High-Precision Delta-Sigma ADC and On-Chip EC-PC Spike Processor.

Authors:  Jian Xu; Anh Tuan Nguyen; Tong Wu; Wenfeng Zhao; Diu Khue Luu; Zhi Yang
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2020-02-06       Impact factor: 3.833

4.  A Bidirectional Neural Interface IC With Chopper Stabilized BioADC Array and Charge Balanced Stimulator.

Authors:  Elliot Greenwald; Ernest So; Qihong Wang; Mohsen Mollazadeh; Christoph Maier; Ralph Etienne-Cummings; Gert Cauwenberghs; Nitish Thakor
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2016-11-08       Impact factor: 3.833

5.  A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability.

Authors:  Yingqiu Cao; Nikolai Rakhilin; Philip H Gordon; Xiling Shen; Edwin C Kan
Journal:  J Neurosci Methods       Date:  2015-12-21       Impact factor: 2.390

6.  A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates.

Authors:  Hyochan An; Samuel R Nason-Tomaszewski; Jongyup Lim; Kyumin Kwon; Matthew S Willsey; Parag G Patil; Hun-Seok Kim; Dennis Sylvester; Cynthia A Chestek; David Blaauw
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2022-07-12       Impact factor: 5.234

Review 7.  Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

Authors:  Kian Ann Ng; Elliot Greenwald; Yong Ping Xu; Nitish V Thakor
Journal:  Med Biol Eng Comput       Date:  2016-01-22       Impact factor: 2.602

8.  Can One Concurrently Record Electrical Spikes from Every Neuron in a Mammalian Brain?

Authors:  David Kleinfeld; Lan Luan; Partha P Mitra; Jacob T Robinson; Rahul Sarpeshkar; Kenneth Shepard; Chong Xie; Timothy D Harris
Journal:  Neuron       Date:  2019-09-05       Impact factor: 17.173

9.  A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro.

Authors:  Marco Ballini; Jan Müller; Paolo Livi; Yihui Chen; Urs Frey; Alexander Stettler; Amir Shadmani; Vijay Viswam; Ian Lloyd Jones; David Jäckel; Milos Radivojevic; Marta K Lewandowska; Wei Gong; Michele Fiscella; Douglas J Bakkum; Flavio Heer; Andreas Hierlemann
Journal:  IEEE J Solid-State Circuits       Date:  2014-11       Impact factor: 5.013

10.  Efficient universal computing architectures for decoding neural activity.

Authors:  Benjamin I Rapoport; Lorenzo Turicchia; Woradorn Wattanapanitch; Thomas J Davidson; Rahul Sarpeshkar
Journal:  PLoS One       Date:  2012-09-12       Impact factor: 3.240

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