A microelectrode array with 8,640 electrodes enabling simultaneous full-frame readout at 6.5 kfps and 112-channel switch-matrix readout at 20 kS/s

Yuan, Xinyue; Kim, S.; Juyon, J.; D'Urbino, Michele; Bullmann, Torsten; Chen, Y.; Stettler, Alexander; Hierlemann, Andreas; Frey, Urs

doi:10.1109/vlsic.2016.7573558

Cited by 10 publications

(14 citation statements)

References 2 publications

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“…The pixel size could be reduced by reducing the transistor size or removing some of the circuit elements; however, the noise would then be increased. 15 The in-pixel circuits must be simplified dramatically to circuits comprised of a buffer amplifier and switching transistor only, as shown in Fig. 1(c), 5,10 so as to reduce the pixel pitch in the same manner as SM implementation.…”

Section: Problems and Objectivesmentioning

confidence: 99%

“…As mentioned in Sec. II, the channel number or the electrode density are limited for SM implementation 12,15 or APS implementation with a complex inpixel circuit, [13][14][15] respectively. The complexity of the in-pixel circuit is indicated by the number of analog transistors (Tr.)…”

Section: F402-4 Ogi Et Almentioning

confidence: 99%

“…10, and further, was comparable to those reported in previous works involving APS with a complex in-pixel circuit. 14,15 This lower noise level could be achieved by optimizing the design of the sensing-amplification transistor and by improving the circuit configuration, as mentioned in Sec. III.…”

Section: F402-4 Ogi Et Almentioning

confidence: 99%

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Twenty-four-micrometer-pitch microelectrode array with 6912-channel readout at 12 kHz via highly scalable implementation for high-spatial-resolution mapping of action potentials

Ogi¹,

Kato²,

Matoba³

et al. 2017

Biointerphases

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A 24-μm-pitch microelectrode array (MEA) with 6912 readout channels at 12 kHz and 23.2-μVrms random noise is presented. The aim is to reduce noise in a “highly scalable” MEA with a complementary metal-oxide-semiconductor integration circuit (CMOS-MEA), in which a large number of readout channels and a high electrode density can be expected. Despite the small dimension and the simplicity of the in-pixel circuit for the high electrode-density and the relatively large number of readout channels of the prototype CMOS-MEA chip developed in this work, the noise within the chip is successfully reduced to less than half that reported in a previous work, for a device with similar in-pixel circuit simplicity and a large number of readout channels. Further, the action potential was clearly observed on cardiomyocytes using the CMOS-MEA. These results indicate the high-scalability of the CMOS-MEA. The highly scalable CMOS-MEA provides high-spatial-resolution mapping of cell action potentials, and the mapping can aid understanding of complex activities in cells, including neuron network activities.

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Section: Problems and Objectivesmentioning

confidence: 99%

Section: F402-4 Ogi Et Almentioning

confidence: 99%

See 1 more Smart Citation

Twenty-four-micrometer-pitch microelectrode array with 6912-channel readout at 12 kHz via highly scalable implementation for high-spatial-resolution mapping of action potentials

Ogi¹,

Kato²,

Matoba³

et al. 2017

Biointerphases

View full text Add to dashboard Cite

show abstract

“…Multiple such probes are often combined into arrays to cover a larger volume in tandem. For ex vivo use (e.g., retinal sections), planar, two-dimensional multi-electrode arrays (MEAs) are common, allowing channel counts of up to tens of thousands (Eversmann et al, 2003;Litke et al, 2004;Berdondini et al, 2005;Yuan et al, 2016;Tsai et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

SpikeForest: reproducible web-facing ground-truth validation of automated neural spike sorters

Magland

Jun

Lovero

et al. 2020

Preprint

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Spike sorting is a crucial but time-intensive step in electrophysiological studies of neuronal activity. While there are many popular software packages for spike sorting, there is little consensus about which are the most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of electrophysiological recordings with ground truth, displaying results interactively on a continuously-updating website. With contributions from over a dozen participating laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include extracellular recordings paired with intracellular voltages, state-of-the-art simulated recordings, and hybrid synthetic datasets. Ten of the most frequently used modern spike sorting codes are wrapped under a common Python framework and evaluated on a compute cluster using an automated pipeline. SpikeForest validates and documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.

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“…Traditionally, a small number of electrodes (channels) are used to monitor spiking activity from a few neurons simultaneously. Recent progress in microfabrication now allows for extracellular recordings from thousands of neurons using microelectrode arrays (MEAs), which have thousands of closely spaced electrodes [13,2,14,1,36,55,32,25,12]. These recordings provide insights that cannot be obtained by pooling multiple single-electrode recordings [27].…”

Section: Introductionmentioning

confidence: 99%

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference

Hurwitz

Srivastava

et al. 2019

Preprint

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Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry. In this work, we present a Bayesian modelling approach for localizing the source of individual spikes on high-density, microelectrode arrays. To allow for scalable inference, we implement our model as a variational autoencoder and perform amortized variational inference. We evaluate our method on both biophysically realistic simulated and real extracellular datasets, demonstrating that it is more accurate than and can improve spike sorting performance over heuristic localization methods such as center of mass.

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A microelectrode array with 8,640 electrodes enabling simultaneous full-frame readout at 6.5 kfps and 112-channel switch-matrix readout at 20 kS/s

Cited by 10 publications

References 2 publications

Twenty-four-micrometer-pitch microelectrode array with 6912-channel readout at 12 kHz via highly scalable implementation for high-spatial-resolution mapping of action potentials

Twenty-four-micrometer-pitch microelectrode array with 6912-channel readout at 12 kHz via highly scalable implementation for high-spatial-resolution mapping of action potentials

SpikeForest: reproducible web-facing ground-truth validation of automated neural spike sorters

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference

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