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      Verification of a Rapidly Multiplexed Circuit for Scalable Action Potential Recording

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          Abstract

          This report presents characterizations of in vivo neural recordings performed with a CMOS multichannel neural recording chip that uses rapid multiplexing directly at the electrodes, without any pre-amplification or buffering. Neural recordings were taken from a 16-channel microwire array implanted in rodent cortex, with comparison to a gold-standard commercial bench-top recording system. We were able to record well-isolated threshold crossings from 10 multiplexed electrodes and typical local field potential waveforms from 16, with strong agreement with the standard system (average SNR = 2.59 and 3.07 respectively). For 10 electrodes, the circuit achieves an effective area per channel of 0.0077 mm 2, which is >5x smaller than typical multichannel chips. Extensive characterizations of noise and signal quality are presented and compared to fundamental theory, as well as results from in vivo and in vitro experiments. By demonstrating the validation of rapid multiplexing directly at the electrodes, this report confirms it as a promising approach for reducing circuit area in massively-multichannel neural recording systems, which is crucial for scaling recording site density and achieving large-scale sensing of brain activity with high spatiotemporal resolution.

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          Most cited references21

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          The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes.

          Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.
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            Large-scale recording of neuronal ensembles.

            How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.
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              How advances in neural recording affect data analysis.

              Over the last five decades, progress in neural recording techniques has allowed the number of simultaneously recorded neurons to double approximately every 7 years, mimicking Moore's law. Such exponential growth motivates us to ask how data analysis techniques are affected by progressively larger numbers of recorded neurons. Traditionally, neurons are analyzed independently on the basis of their tuning to stimuli or movement. Although tuning curve approaches are unaffected by growing numbers of simultaneously recorded neurons, newly developed techniques that analyze interactions between neurons become more accurate and more complex as the number of recorded neurons increases. Emerging data analysis techniques should consider both the computational costs and the potential for more accurate models associated with this exponential growth of the number of recorded neurons.
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                Author and article information

                Contributors
                Role: Member, IEEE
                Role: Member, IEEE
                Journal
                101312520
                36384
                IEEE Trans Biomed Circuits Syst
                IEEE Trans Biomed Circuits Syst
                IEEE transactions on biomedical circuits and systems
                1932-4545
                1940-9990
                11 August 2020
                09 December 2019
                December 2019
                09 December 2020
                : 13
                : 6
                : 1655-1663
                Affiliations
                Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112 USA
                Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112 USA
                Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112 USA
                Author notes
                ( Corresponding author: Ross Martin Walker. ross.walker@ 123456utah.edu ).
                Author information
                http://orcid.org/0000-0003-0252-3289
                http://orcid.org/0000-0002-4158-0158
                Article
                NIHMS1548210
                10.1109/TBCAS.2019.2958348
                7454001
                31825873
                eeec07f9-813d-4823-9bc4-19f71f02c31c

                This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

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                action potential,analog design,electrode,electrode array,neural amplifier,neural engineering,neural recording,neuroscience,windowed integrator sampling

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