CMOS-Based High-Density Microelectrode Arrays: Technology and Applications

Obien, Marie Engelene J.; Gong, Wei; Frey, Urs; Bakkum, Douglas J.

doi:10.1007/978-981-10-3957-7_1

Cited by 6 publications

(6 citation statements)

References 100 publications

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“…Probes host hundreds of recording sites in 5 mm length [4] and almost one thousand in 10 mm [5]. Finally, High-density MEAs (HDMEAs) permit to retrieve information at the single cell level [6], to study electrical-and light-evoked neural response and to acquire from tens of thousands recording sites. For example, [7] features 4096 recording sites, and [8] features 65,536 recording sites.…”

Section: Introductionmentioning

confidence: 99%

ZyON: Enabling Spike Sorting on APSoC-Based Signal Processors for High-Density Microelectrode Arrays

2020

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Multi-Electrode Arrays and High-Density Multi-Electrode Arrays of sensors are a key instrument in neuroscience research. Such devices are evolving to provide ever-increasing temporal and spatial resolution, paving the way to unprecedented results when it comes to understanding the behaviour of neuronal networks and interacting with them. However, in some experimental cases, in-place lowlatency processing of the sensor data acquired by the arrays is required. This poses the need for highperformance embedded computing platforms capable of processing in real-time the stream of samples produced by the acquisition front-end to extract higher-level information. Previous work has demonstrated that Field-Programmable Gate Array and All-Programmable System-On-Chip devices are suitable target technology for the implementation of real-time processors of High-Density Multi-Electrode Arrays data. However, approaches available in literature can process a limited number of channels or are designed to execute only the first steps of the neural signal processing chain. In this work, we propose an All-Programmable System-On-Chip based implementation capable of sorting neural spikes acquired by the sensors, to associate the shape of each spike to a specific firing neuron. Our system, implemented on a Xilinx Z7020 All-Programmable System-On-Chip is capable of executing on-line spike sorting up to 5500 acquisition channels, 43x more than state-of-the-art alternatives, supporting 18KHz acquisition frequency. We present an experimental study on a commonly used reference dataset, using on-line refinement of the sorting clusters to improve accuracy up to 82%, with only 4% degradation with respect to off-line analysis. INDEX TERMS Field programmable gate arrays, Signal processing, Neural engineering, APSoC, HDMEA, Spike sorting.

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Section: Introductionmentioning

confidence: 99%

ZyON: Enabling Spike Sorting on APSoC-Based Signal Processors for High-Density Microelectrode Arrays

2020

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“…High-density microelectrode arrays (HD-MEAs) are important tools in electrophysiology research and are used to simultaneously record the electrical activity of large numbers of neurons. Recent advances in complementary metal-oxide-semiconductor (CMOS) technology have increased the number of simultaneously active recording electrodes from a few hundred to several thousand per chip (Berdondini et al 2009; Frey et al 2010; Johnson et al 2012; Müller et al 2013; Bertotti et al 2014; Viswam et al 2016; Obien et al 2017). At the same time, the center-to-center electrode distances (pitch) have decreased significantly to a point where the electrode density comes close to or even exceeds the density of neurons in certain tissues, for example, the density of ganglion cells in the murine retina (Fiscella et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Automatic spike sorting for high-density microelectrode arrays

Diggelmann

Fiscella

Hierlemann

et al. 2018

Journal of Neurophysiology

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High-density microelectrode arrays (HD-MEAs) can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike-sorters have to be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multi-electrodes, however, suffer from the "curse of dimensionality", and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal-component-analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike-sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently using classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data pre-whitening before the principal-component-analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and which were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation were not required.

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“…Microelectrode arrays (MEAs) have been used in neuroscience since the very early 80 s to study the mechanisms underlying the dynamics of neural networks and brain circuits [1]. Thanks to major technological advancements occurred in this field in the last decade, CMOS-based MEAs capable of simultaneously record from thousands of densely integrated sensing electrodes are nowadays increasingly used to monitor the electrical activity of large neuronal populations with fine spatio-temporal resolution either in vitro or, more recently, in vivo [2]. Furthermore, these devices can integrate in the same chip both recording and stimulating electrodes or can be combined with external electrical or optical stimulation, thus enabling to perturb neural systems for studying their Giovanni Pietro Seu is with the Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, 16145 Genoa (Italy), and also with the Deparment of Electrical and Electronic Engineering (DIEE) at the University of Cagliari, Piazza D'Armi, 09123 Cagliari (Italy) (e-mail: giovanni.seu@diee.unica.it).…”

Section: Introductionmentioning

confidence: 99%

Exploiting All Programmable SoCs in Neural Signal Analysis: A Closed-Loop Control for Large-Scale CMOS Multielectrode Arrays

Seu

Angotzi

Boi

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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Microelectrode array (MEA) systems with up to several thousands of recording electrodes and electrical or optical stimulation capabilities are commercially available or described in the literature. By exploiting their submillisecond and micrometric temporal and spatial resolutions to record bioelectrical signals, such emerging MEA systems are increasingly used in neuroscience to study the complex dynamics of neuronal networks and brain circuits. However, they typically lack the capability of implementing real-time feedback between the detection of neuronal spiking events and stimulation, thus restricting large-scale neural interfacing to open-loop conditions. In order to exploit the potential of such large-scale recording systems and stimulation, we designed and validated a fully reconfigurable FPGA-based processing system for closed-loop multichannel control. By adopting a Xilinx Zynq-all-programmable system on chip that integrates reconfigurable logic and a dual-core ARM-based processor on the same device, the proposed platform permits low-latency preprocessing (filtering and detection) of spikes acquired simultaneously from several thousands of electrode sites. To demonstrate the proposed platform, we tested its performances through ex vivo experiments on the mice retina using a state-of-the-art planar high-density MEA that samples 4096 electrodes at 18 kHz and record light-evoked spikes from several thousands of retinal ganglion cells simultaneously. Results demonstrate that the platform is able to provide a total latency from whole-array data acquisition to stimulus generation below 2 ms. This opens the opportunity to design closed-loop experiments on neural systems and biomedical applications using emerging generations of planar or implantable large-scale MEA systems.

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CMOS-Based High-Density Microelectrode Arrays: Technology and Applications

Cited by 6 publications

References 100 publications

ZyON: Enabling Spike Sorting on APSoC-Based Signal Processors for High-Density Microelectrode Arrays

ZyON: Enabling Spike Sorting on APSoC-Based Signal Processors for High-Density Microelectrode Arrays

Automatic spike sorting for high-density microelectrode arrays

Exploiting All Programmable SoCs in Neural Signal Analysis: A Closed-Loop Control for Large-Scale CMOS Multielectrode Arrays

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