daohang fenxiangbox searchbox qikanlogonew daohangnew searchboxnew navrightzone footerzone paper

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

Universidade do Porto; Universidade do Porto; Universidade do Porto; Universidade do Porto; Universidade do Porto
AITranslate
Publisher: ACS
Share Citation Information Add to Favorites

    Scan to share on WeChat or Moments

Use WeChat scan.
Share with WeChat friends or Moments

Abstract AITranslate

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.

KeyWords AITranslate

hybrid bioelectronic systems memristors realtime control neuroprosthesis neuromodulation implantable devices in vitroShow More
No data

Basic Information:

DOI:https://doi.org/10.1021/acsaelm.2c00198

Chinese Library Classification Number:

Citation Information:

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.

quote

GB/T 7714-2015 [1] Catarina Dias, Domingos Castro, Miguel Aroso, et al. ACS Applied Electronic Materials, 2022(4). DOI:10.1021/acsaelm.2c00198.
MLA [1] Catarina Dias, et al., ACS Applied Electronic Materials, no. 4, 2022, https://doi.org/10.1021/acsaelm.2c00198.
APA [1] Catarina Dias, Domingos Castro, Miguel Aroso, João Ventura, & Paulo Aguiar. (2022). ACS Applied Electronic Materials(4). https://doi.org/10.1021/acsaelm.2c00198
IEEE [1] Catarina Dias, Domingos Castro, Miguel Aroso, João Ventura, and Paulo Aguiar, ACS Applied Electronic Materials, no. 4, 2022, doi: 10.1021/acsaelm.2c00198. keywords: {hybrid bioelectronic systems memristors realtime control neuroprosthesis neuromodulation implantable devices in vitroShow More}