Real-Time Digital Implementation of a Principal Component Analysis Algorithm for Neurons Spike Detection

被引：0

作者：

Vallicelli, E. A. ^{[1
]}

Fary, F. ^{[1
]}

Baschirotto, A. ^{[1
]}

de Matteis, M. ^{[1
]}

Reato, M. ^{[3
]}

Maschietto, M. ^{[2
]}

Rocchi, F. ^{[2
]}

Vassanelli, S. ^{[2
]}

Guarrera, D. ^{[3
]}

Collazuol, G. ^{[3
]}

Zeitler, R. ^{[4
]}

机构：

[1] Univ Milano Bicocca, Dept Phys, Milan, Italy

[2] Univ Padua, Dept Biomed Sci, Padua, Italy

[3] Univ Padua, Dept Phys, Padua, Italy

[4] Venneos GmbH, Stuttgart, Germany

来源：

2018 INTERNATIONAL CONFERENCE ON IC DESIGN AND TECHNOLOGY (ICICDT 2018) | 2018年

关键词：

Biological neural networks; Biosensors; Digital Circuits; Field programmable gate arrays; Principal component analysis; STIMULATION; ARRAY;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents the result of a multidisciplinary experiment where electrical activity from a cultured rat hippocampi neuronal population is detected in real time by a FPGA implemented digital circuit. State-of-the-art EOMOSFET Multi Electrode Array (MEA) biosensors exploits a capacitive coupling between the biological environment and the sensing electronics to minimize invasiveness and cell damage, at the price of a lower SNR. For this reason, they are typically improved by noise rejection algorithms. Real time neural spikes detection opens unthinkable scenarios, allowing to stimulate single neurons in response to their behavior, possibly improving medical conditions like epilepsy. In this scenario, a spike sorting algorithm has been hardware implemented, allowing real time neural spike detection with a latency of 165ns.

引用

收藏

页码：33 / 36

页数：4

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