A high-performance asynchronous readout circuit with cascade function for a neural recording micro-electrode array

IF 0.7 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

Electronics Letters Pub Date : 2024-09-25 DOI:10.1049/ell2.70028

Jiaqi Feng, Yujie Cai, Leilei Huang

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引用次数: 0

Abstract

The data-driven machine learning technology used for neural decoding emphasizes the requirements of in vivo and in vitro neural signal acquisition with high spatial and temporal resolution. However, micro-electrode arrays (MEAs) that simultaneously achieve high spatial and temporal resolution neural signal acquisition are not yet available. Meanwhile, the high data bandwidth of large-scale MEA brings challenges in power consumption, data transmission, storage, and neural signal processing. This research aims to improve the spatial and temporal resolution of MEA, reduce the cost and time of large-scale MEA customization through cascading, and reduce the bandwidth of large-scale MEA through spike compression. Firstly, based on in-pixel spike detection, a row-based neural spike readout mechanism and related array circuit to improve the neural spike readout performance and temporal resolution of neural signal acquisition is proposed. To further enhance the spatial resolution and reduce the risk of large-scale MEA fabrication, the cascading capability of the readout circuit is explored. Lastly, spatial correlation-based neural spike encoding is proposed to reduce the data bandwidth, achieving a 5.2× compression rate. This is a study on implementing large-scale MEA through cascaded readout circuits and novel study to utilize the spatial correlation between detected neural spikes for further compression.

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用于神经记录微电极阵列的具有级联功能的高性能异步读出电路

用于神经解码的数据驱动型机器学习技术强调体内和体外高时空分辨率神经信号采集的要求。然而，目前还没有同时实现高空间和时间分辨率神经信号采集的微电极阵列（MEA）。同时，大规模 MEA 的高数据带宽给功耗、数据传输、存储和神经信号处理带来了挑战。本研究旨在提高 MEA 的空间和时间分辨率，通过级联降低大规模 MEA 定制的成本和时间，并通过尖峰压缩降低大规模 MEA 的带宽。首先，在像素内尖峰检测的基础上，提出了一种基于行的神经尖峰读出机制和相关阵列电路，以提高神经信号采集的神经尖峰读出性能和时间分辨率。为了进一步提高空间分辨率并降低大规模 MEA 制造的风险，还探讨了读出电路的级联能力。最后，提出了基于空间相关性的神经尖峰编码，以降低数据带宽，实现 5.2 倍的压缩率。这是一项通过级联读出电路实现大规模 MEA 的研究，也是一项利用检测到的神经尖峰之间的空间相关性进行进一步压缩的新颖研究。

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来源期刊

Electronics Letters 工程技术-工程：电子与电气

CiteScore

2.70

自引率

0.00%

发文量

268

审稿时长

3.6 months

期刊介绍： Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews. Scope As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below. Antennas and Propagation Biomedical and Bioinspired Technologies, Signal Processing and Applications Control Engineering Electromagnetism: Theory, Materials and Devices Electronic Circuits and Systems Image, Video and Vision Processing and Applications Information, Computing and Communications Instrumentation and Measurement Microwave Technology Optical Communications Photonics and Opto-Electronics Power Electronics, Energy and Sustainability Radar, Sonar and Navigation Semiconductor Technology Signal Processing MIMO