@inproceedings{dea37dbf1ecb465cad16d8d4e257103a,
title = "Tone Stimulus Detection For Rats Using RRAM-Based Local Field Potential Monitoring",
abstract = "The comprehension of brain activity presents significant challenges in the field of neuroscience. Contrary to spikes, Local Field Potentials (LFPs) present improved stability acquisition in chronic implant scenarios and potential reductions in sampling and processing rates. While existing electrophysiology acquisition systems focus predominantly on spike detection and sorting, there is a lack of real-time tools for exploiting LFPs. To address this gap, we present a Resistive-RAM (RRAM) based approach to process LFP traces. Our method follows an improved Memristive Integrating Sensor (MIS) protocol to effectively detect LFP events recorded from the deep-brain of an awake rat, while externally stimulated by a tone. Experimental results demonstrate the feasibility of real-time neural activity processing, offering insights into detecting meaningful external stimuli and facilitating efficient neural state estimation.",
keywords = "RRAM, bio-signal processing, edge processing, local field potential (LFP), memristor, real-time detection",
author = "Caterina Sbandati and Spyros Stathopoulos and Patrick Foster and Peer, {Noam D.} and Alexantrou Serb and Shiwei Wang and Dana Cohen and Themis Prodromakis",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023 ; Conference date: 19-10-2023 Through 21-10-2023",
year = "2023",
doi = "10.1109/BioCAS58349.2023.10388917",
language = "الإنجليزيّة",
series = "BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings",
address = "الولايات المتّحدة",
}