Electrical modeling of the cell–electrode interface for recording neural activity from high-density microelectrode arrays

Joye, Neil; Schmid, Alexandre; Leblebici, Yusuf

doi:10.1016/j.neucom.2009.09.006

Cited by 64 publications

(61 citation statements)

References 37 publications

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“…Common models to calculate the electrical properties of a given cell-sensor interface are the point-contact and the area-contact models [26,30,31,35,36]. Both models represent elements of the membrane, the electrode, and the chip either as resistors or capacitors.…”

Section: Sensor Impedancementioning

confidence: 99%

“…The spatial resolution of such nanocavity sensors is preserved by the small aperture which connects the nanofluidic cavity with the chip's surface. Modeling the signal transduction at the cell-sensor interface is important to optimize the system's design and to understand how a cell's action potential is related to the measured signal [25][26][27]. In this paper we present a model to describe the impedance and the cell-sensor coupling of a nanocavity electrode.…”

Section: Introductionmentioning

confidence: 99%

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Frequency-dependent signal transfer at the interface between electrogenic cells and nanocavity electrodes

et al. 2012

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We present a model to describe the response of chip-based nanocavity sensors during extracellular recording of action potentials. These sensors feature microelectrodes which are embedded in liquid-filled cavities. They can be used for the highly localized detection of electrical signals on a chip. We calculate the sensor's impedance and simulate the propagation of action potentials. Subsequently we apply our findings to analyze cell-chip coupling properties. The results are compared to experimental data obtained from cardiomyocyte-like cells. We show that both the impedance and the modeled action potentials fit the experimental data well. Furthermore, we find evidence for a large seal resistance of cardiomyocytes on nanocavity sensors compared to conventional planar recording systems.

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Section: Sensor Impedancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frequency-dependent signal transfer at the interface between electrogenic cells and nanocavity electrodes

et al. 2012

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“…The SNR values of the recorded signals have been defined using cellelectrode models adapted from [4] and [5]. The noise factor F P S of the pixel stage is depicted in Fig.…”

Section: Resultsmentioning

confidence: 99%

Amplitude modulation based readout for very dense active microelectrode arrays

Joye

Schmid

Leblebici

2011

IEICE Electron. Express

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Abstract:A readout method which is suitable for high-density active microelectrode arrays used in electrophysiology experiments is presented. Amplitude modulation of consecutive channels enables simultaneous recording and transmission of the signals recorded on multiple electrodes, using a single amplifier. The physical limitation of the readout method is demonstrated to relate to the summation of the thermal noise of each recorded signal at the input of the amplification stage. Post-layout simulations of a possible circuit implementation in a 0.18 µm CMOS technology show that five pixels connected to a single amplifier are feasible with a pitch dimension of 17 µm, and an SNR value of 15 dB.

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“…This is still a subject of intense investigation that had led to a significant number of publications in the last two decades [144][145][146][147][148][149][150][151][152]. …”

Section: Modelling Neural Interfacesmentioning

confidence: 99%

Flexible and Organic Neural Interfaces: A Review

Lago

Cester

2017

Applied Sciences

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Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.Abstract: Neural interfaces are a fundamental tool to interact with neurons and to study neural networks by transducing cellular signals into electronics signals and vice versa. State-of-the-art technologies allow both in vivo and in vitro recording of neural activity. However, they are mainly made of stiff inorganic materials that can limit the long-term stability of the implant due to infection and/or glial scars formation. In the last decade, organic electronics is digging its way in the field of bioelectronics and researchers started to develop neural interfaces based on organic semiconductors, creating more flexible and conformable neural interfaces that can be intrinsically biocompatible. In this manuscript, we are going to review the latest achievements in flexible and organic neural interfaces for the recording of neuronal activity.

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Electrical modeling of the cell–electrode interface for recording neural activity from high-density microelectrode arrays

Cited by 64 publications

References 37 publications

Frequency-dependent signal transfer at the interface between electrogenic cells and nanocavity electrodes

Frequency-dependent signal transfer at the interface between electrogenic cells and nanocavity electrodes

Amplitude modulation based readout for very dense active microelectrode arrays

Flexible and Organic Neural Interfaces: A Review

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