A CMOS-based sensor array for in-vitro neural tissue interfacing with 4225 recording sites and 1024 stimulation sites

Bertotti, Gabriel; Velychko, Dmytro; Dodel, Norman; Keil, Stefan; Wolansky, D.; Tillak, Bernd; Schreiter, Matthias; Grall, Andreas; Jesinger, Peter; RöHler, S.; Eickenscheidt, Max; Stett, Alfred; Möller, Andreas; Boven, Karl-Heinz; Zeck, Günther; Thewes, R.

doi:10.1109/biocas.2014.6981723

Cited by 59 publications

(57 citation statements)

References 6 publications

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“…The use of CMOS technology helps to overcome the connectivity problem of how to interface thousands of tightly-spaced electrodes, while, at the same time, it improves signal-to-noise characteristics, as signal conditioning is done on chip next to where the partially very small signals (< 10 μV) are generated (10)(11)(12). Here, we demonstrate how CMOS high-density microelectrode arrays (HD-MEAs) featuring several thousands of transducers (> 3'000 transducers per mm 2 ) can be used to record from or stimulate potentially any individual neuron or subcellular compartment on the CMOS chip (10)(11)(12)(13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

Highly integrated CMOS microsystems to interface with neurons at subcellular resolution

Hierlemann

Müller

Bakkum

et al. 2015

2015 IEEE International Electron Devices Meeting (IEDM)

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CMOS high-density transducer arrays enable fundamentally new neuroscientific insights through, e.g., facilitating investigation of axonal signaling characteristics, with the "axonal" side of neuronal activity being largely inaccessible to established methods. They also enable high-throughput monitoring of potentially all action potentials in a larger neuronal network (> 1000 neurons) over extended time to see developmental effects or effects of disturbances. Applications include research in neural diseases and pharmacology.

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Section: Introductionmentioning

confidence: 99%

Highly integrated CMOS microsystems to interface with neurons at subcellular resolution

Hierlemann

Müller

Bakkum

et al. 2015

2015 IEEE International Electron Devices Meeting (IEDM)

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“…CMOS (complementary metal oxide semiconductor) technology-based planar, high-density microelectrode arrays (HD-MEAs) feature several thousands of transducers at densities of > 3'000 transducers per mm 2 [4,18,[21][22][23][24][25][26] (Figure 1), which enables recordings from large neuronal networks at single-cell, or even subcellular-component resolution [27,28]. The integrated systems feature tightly-spaced transducers and the respective addressing and readout circuitry on a single chip [4,[21][22][23][24][25][26].…”

Section: Cmos High-density Systemsmentioning

confidence: 99%

“…1a, have been pursued [21,22,24,26]. In the case of open-gate FET transducers, additional stimulation spots are needed and distributed between the electrodes.…”

Section: Cmos High-density Systemsmentioning

confidence: 99%

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Direct interfacing of neurons to highly integrated microsystems

Hierlemann

2017

2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS)

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The use of large high-density transducer arrays enables fundamentally new neuroscientific insights through enabling high-throughput monitoring of action potentials of larger neuronal networks (> 1000 neurons) over extended time to see effects of disturbances or developmental effects, and through facilitating detailed investigations of neuronal signaling characteristics at subcellular level, for example, the study of axonal signal propagation that has been largely inaccessible to established methods. Applications include research in neural diseases and pharmacology.

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“…Upcoming technologies will increase this number significantly in near future [8]. There has also been progress in simultaneously recording and stimulating the neurons [9], [10], [11]. These advancements will soon alleviate the issue of data availability.…”

Section: Introductionmentioning

confidence: 99%

Learning network structures from firing patterns

Karbasi¹,

Salavati

Vetteri

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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How can we decipher the hidden structure of a network based on limited observations? This question arises in many scenarios ranging from social to wireless and to neural networks. In such settings, we typically observe the nodes' behaviors (e.g., the time a node learns about a piece of information, or the time a node gets infected by a disease), and we are interested in inferring the true network over which the diffusion takes place. In this paper, we consider this problem over a neural network where our aim is to reconstruct the connectivity between neurons merely by observing their firing activity. We develop an iterative NEUral INFerence algorithm NEUINF to identify the type of effective neural connections (i.e. excitatory/inhibitory) based on the Perceptron learning rule. We provide theoretical bounds on the average performance of NEUINF as well as numerical analysis to compare the performance of the proposed approach to some previous art.

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A CMOS-based sensor array for in-vitro neural tissue interfacing with 4225 recording sites and 1024 stimulation sites

Cited by 59 publications

References 6 publications

Highly integrated CMOS microsystems to interface with neurons at subcellular resolution

Highly integrated CMOS microsystems to interface with neurons at subcellular resolution

Direct interfacing of neurons to highly integrated microsystems

Learning network structures from firing patterns

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