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Kim, Seong-Jin
Bio-inspired Microsystems Lab (BiML)
Research Interests
  • Integrated analog-mixed signal circuit design, semiconductor sensor interface circuits


A Multimodal Neural Activity Readout Integrated Circuit for Recording Fluorescence and Electrical Signals

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A Multimodal Neural Activity Readout Integrated Circuit for Recording Fluorescence and Electrical Signals
Lee, TaejuPark, Jee-HoChou, NamsunCho, Il-JooKim, Seong-JinJe, Minkyu
Issue Date
Institute of Electrical and Electronics Engineers Inc.
IEEE ACCESS, v.9, pp.118610 - 118623
Monitoring the electrical neural signals is an important method for understanding the neuronal mechanism. In particular, in order to perform a cell-type-specific study, it is necessary to observe the concentration of calcium ions using fluorescent indicators in addition to measuring the electrical neural signal. This paper presents a multimodal multichannel neural activity readout integrated circuit that can perform not only electrical neural recording but also fluorescence recording of neural activity for the cell-type-specific study of heterogeneous neuronal cell populations. For monitoring the calcium ions, the photodiode generates the current according to the fluorescence expressed by the reaction between the genetically encoded calcium indicators and calcium ions. The time-based fluorescence recording circuit then records the photodiode current. The electrical neural signal captured by the microelectrode is recorded through the low-noise amplifier, variable gain amplifier, and analog-to-digital converter. The proposed integrated circuit is fabricated in a 1-poly 6-metal (1P6M) 0.18- μm CMOS process. The fluorescence recording circuit achieves a recording range of 81 dB (75 pA to 860 nA) and consumes a power of 724 nW/channel. The electrical recording circuit achieves an input-referred noise of 2.7 μVrms over the bandwidth of 10 kHz, while consuming the power of 4.9 μW /channel. The functionality of the proposed circuits is verified through the in vivo and in vitro experiments. Compared to the conventional neuroscience tools, which consist of bulky off-chip components, this neural interface is implemented in a compact size to perform multimodal neural recording while consuming low power.
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