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Memristor-Based Neuromodulation Device for Real-Time Monitoring and Adaptive Control of Neuronal Populations

Authors :
Catarina Dias
Domingos Castro
Miguel Aroso
João Ventura
Paulo Aguiar
Source :
ACS Applied Electronic Materials. 4:2380-2387
Publication Year :
2022
Publisher :
American Chemical Society (ACS), 2022.

Abstract

Neurons are specialized cells for information transmission and information processing. In fact, many neurologic disorders are directly linked not to cellular viability/homeostasis issues but rather to specific anomalies in electrical activity dynamics. Consequently, therapeutic strategies based on the direct modulation of neuronal electrical activity have been producing remarkable results, with successful examples ranging from cochlear implants to deep brain stimulation. Developments in these implantable devices are hindered, however, by important challenges such as power requirements, size factor, signal transduction, and adaptability/computational capabilities. Memristors, neuromorphic nanoscale electronic components able to emulate natural synapses, provide unique properties to address these constraints, and their use in neuroprosthetic devices is being actively explored. Here, we demonstrate, for the first time, the use of memristive devices in a clinically relevant setting where communication between two neuronal populations is conditioned to specific activity patterns in the source population. In our approach, the memristor device performs a pattern detection computation and acts as an artificial synapse capable of reversible short-term plasticity. Using in vitro hippocampal neuronal cultures, we show real-time adaptive control with a high degree of reproducibility using our monitor-compute-actuate paradigm. We envision very similar systems being used for the automatic detection and suppression of seizures in epileptic patients.

Details

ISSN :
26376113
Volume :
4
Database :
OpenAIRE
Journal :
ACS Applied Electronic Materials
Accession number :
edsair.doi.dedup.....66de16e35a2817437ed70f897f9510d8
Full Text :
https://doi.org/10.1021/acsaelm.2c00198