A Fully Integrated 64-Channel Recording System for Extracellular Raw Neural Signals

Zhang, Xiangwei; Li, Quan; Chen, Chengying; Li, Yan; Fu-qiang, Zuo; Liu, Xin; Zhang, Hao; Wang, Xiaosong; Liu, Yu

doi:10.3390/electronics10212726

Cited by 3 publications

(1 citation statement)

References 34 publications

(109 reference statements)

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“…There are two main implementation methods for low-noise IAs. The first method utilizes a capacitance amplifier, forming a high-pass filter with capacitance and pseudo-resistance, with a 3 dB cutoff frequency below 1 Hz, which is able to eliminate EDO rail-to-rail [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. This amplifier structure is simple and widely used.…”

Section: Introductionmentioning

confidence: 99%

A 12 μW 10 kHz BW 58.9 dB SNDR AC-Coupled Incremental ADC for Neural Recording

Zhang,

Hou,

Wang

et al. 2024

Electronics

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This paper presents an AC-coupled, incremental analog-to-digital converter (ADC) based on two-step quantization for high-density implantable neural recording. It achieves a rail-to-rail electrode DC offset (EDO) rejection, low noise, a small area, and low power consumption. Fabricated in a 180 nm CMOS process, the prototype ADC achieves a high input impedance, 24 mVpp linear input range, and 58.9 dB signal-to-noise and distortion ratio (SNDR). Its core circuit has a power consumption of 12 μW and an area of 0.0192 mm2. The referred-to-input (RTI) noise is 6.9 μVrms within the bandwidth of 1 Hz–10 kHz.

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Section: Introductionmentioning

confidence: 99%

A 12 μW 10 kHz BW 58.9 dB SNDR AC-Coupled Incremental ADC for Neural Recording

Zhang,

Hou,

Wang

et al. 2024

Electronics

Self Cite

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Inkube: An all-in-one solution for neuron culturing, electrophysiology, and fluidic exchange

Maurer,

Fassbind,

Ruff

et al. 2024

Preprint

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Culturing neuronal networksin vitrois a tedious and time-consuming endeavor. In addition, how the composition of the culture medium and environmental variables such as temperature, osmolarity, and pH affect the spiking behavior of neuronal cultures is difficult to study using electrophysiology. In this work, we present “inkube”, an incubation system that has been combined with an electrophysiology setup and a fully automatic perfusion system. This setup allows for the precise measurement and control of the temperature of up to 4 microelectrode arrays (MEAs) in parallel. In addition, neuronal activity can be electrically induced and recorded from the MEAs. inkube can continuously monitor the medium level to automatically readjust osmolarity. Using inkube’s unique capability to precisely control the environmental variables of a neural culture, we found that medium evaporation influences the spiking response. Moreover, decreasing medium temperature by only 1.5°C significantly affected spike latency, a measure commonly used to show plasticity inin vitroexperiments. We finally provide a proof-of-concept experiment for drug screening applications, where inkube automatically and precisely varies the concentration of magnesium ions in the medium. Given its high level of autonomy, the system can record, stimulate, and control the medium continuously without user intervention. Both the hardware and the software of inkube are completely open-source.HighlightsLow-cost, open-hardware/open-software electrophysiology setupFull incubation solution: Temperature, CO2, and humidity controlPerfusion system for automatic fluidic exchange and drug testing with volume feedback

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Approximate Computing-Based Processing of MEA Signals on FPGA

et al. 2023

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Microelectrode arrays (MEAs) are essential equipment in neuroscience for studying the nervous system’s behavior and organization. MEAs are arrays of parallel electrodes that work by sensing the extracellular potential of neurons in their proximity. Processing the data streams acquired from MEAs is a computationally intensive task requiring parallelization. It is performed using complex signal processing algorithms and architectural templates. In this paper, we propose using approximate computing-based algorithms on Field Programmable Gate Arrays (FPGAs), which can be very useful in custom implementations for processing neural signals acquired from MEAs. The motivation is to provide better performance gains in the system area, power consumption, and latency associated with real-time processing at the cost of reduced output accuracy within certain bounds. Three types of approximate adders are explored in different configurations to develop the signal processing algorithms. The algorithms are used to build approximate processing systems on FPGA and then compare them with the accurate system. All accurate and approximate systems are tested on real biological signals with the same settings. Results show an enhancement in processing speed of up to 37.6% in some approximate systems without a loss in accuracy. In other approximate systems, the area reduction is up to 14.3%. Other systems show the trade between processing speed, accuracy, and area.

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A Fully Integrated 64-Channel Recording System for Extracellular Raw Neural Signals

Cited by 3 publications

References 34 publications

A 12 μW 10 kHz BW 58.9 dB SNDR AC-Coupled Incremental ADC for Neural Recording

A 12 μW 10 kHz BW 58.9 dB SNDR AC-Coupled Incremental ADC for Neural Recording

Inkube: An all-in-one solution for neuron culturing, electrophysiology, and fluidic exchange

Approximate Computing-Based Processing of MEA Signals on FPGA

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