Pharmacologically and Electrically-induced Network-wide Activation of Olfactory Bulb with Large-scale Biosensor

Rossi, Livia; Emery, Brett Addison; Khanzada, Shahrukh; Hu, Xin; Amin, Hayder

doi:10.1109/biosensors58001.2023.10280876

2023 IEEE BioSensors Conference (BioSensors) 2023

DOI: 10.1109/biosensors58001.2023.10280876

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Pharmacologically and Electrically-induced Network-wide Activation of Olfactory Bulb with Large-scale Biosensor

Livia Rossi,

Brett Addison Emery,

Shahrukh Khanzada

et al.

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2024

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DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors

Hu,

Emery,

Khanzada

et al. 2024

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Large-scale multimodal neural recordings on high-density biosensing microelectrode arrays (HD-MEAs) offer unprecedented insights into the dynamic interactions and connectivity across various brain networks. However, the fidelity of these recordings is frequently compromised by pervasive noise, which obscures meaningful neural information and complicates data analysis. To address this challenge, we introduce DENOISING, a versatile data-derived computational engine engineered to adjust thresholds adaptively based on large-scale extracellular signal characteristics and noise levels. This facilitates the separation of signal and noise components without reliance on specific data transformations. Uniquely capable of handling a diverse array of noise types (electrical, mechanical, and environmental) and multidimensional neural signals, including stationary and non-stationary oscillatory local field potential (LFP) and spiking activity, DENOISING presents an adaptable solution applicable across different recording modalities and brain networks. Applying DENOISING to large-scale neural recordings from mice hippocampal and olfactory bulb networks yielded enhanced signal-to-noise ratio (SNR) of LFP and spike firing patterns compared to those computed from raw data. Comparative analysis with existing state-of-the-art denoising methods, employing SNR and root mean square noise (RMS), underscores DENOISING’s performance in improving data quality and reliability. Through experimental and computational approaches, we validate that DENOISING improves signal clarity and data interpretation by effectively mitigating independent noise in spatiotemporally structured multimodal datasets, thus unlocking new dimensions in understanding neural connectivity and functional dynamics.

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DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors

Hu,

Emery,

Khanzada

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

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Pharmacologically and Electrically-induced Network-wide Activation of Olfactory Bulb with Large-scale Biosensor

Cited by 1 publication

References 16 publications

DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors

DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors

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