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Title: Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics

Journal Article · · ACS Nano
ORCiD logo [1]; ORCiD logo [2];  [3];  [3];  [3]; ORCiD logo [4]; ORCiD logo [5]; ORCiD logo [5];  [6]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Joint Institute for Biological Sciences; Univ. of Tennessee, Knoxville, TN (United States). Department of Mechanical, Aerospace and Biomedical Engineering
  2. Univ. of Tennessee, Knoxville, TN (United States). Bredesen Center for Interdisciplinary Research and Department of Mechanical, Aerospace and Biomedical Engineering
  3. Univ. of Tennessee, Knoxville, TN (United States). Department of Electrical Engineering and Computer Science
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)
  6. Univ. of Tennessee, Knoxville, TN (United States). Department of Mechanical, Aerospace and Biomedical Engineering

Solid-state neuromorphic systems based on transistors or memristors have yet to achieve the interconnectivity, performance, and energy efficiency of the brain due to excessive noise, undesirable material properties, and nonbiological switching mechanisms. Here in this work, we demonstrate that an alamethicin-doped, synthetic biomembrane exhibits memristive behavior, emulates key synaptic functions including paired-pulse facilitation and depression, and enables learning and computing. Unlike state-of-the-art devices, our two-terminal, biomolecular memristor features similar structure (biomembrane), switching mechanism (ion channels), and ionic transport modality as biological synapses while operating at considerably lower power. The reversible and volatile voltage-driven insertion of alamethicin peptides into an insulating lipid bilayer creates conductive pathways that exhibit pinched current–voltage hysteresis at potentials above their insertion threshold. Moreover, the synapse-like dynamic properties of the biomolecular memristor allow for simplified learning circuit implementations. Finally, low-power memristive devices based on stimuli-responsive biomolecules represent a major advance toward implementation of full synaptic functionality in neuromorphic hardware.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1468075
Journal Information:
ACS Nano, Vol. 12, Issue 5; ISSN 1936-0851
Publisher:
American Chemical Society (ACS)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 73 works
Citation information provided by
Web of Science

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Improvement of analogue switching characteristics of MoS 2 memristors through plasma treatment journal January 2020
On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits journal July 2019
Artificial water channels enable fast and selective water permeation through water-wire networks journal December 2019
A flexible conformable artificial organ-damage memory system towards hazardous gas leakage based on a single organic transistor journal January 2019
Overview of Resistive Random Access Memory (RRAM): Materials, Filament Mechanisms, Performance Optimization, and Prospects journal May 2019
Ultrathin TiO x Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors journal May 2019
Regulating firing rates in a neural circuit by activating memristive synapse with magnetic coupling journal September 2019
Bio-inspired protonic memristor devices based on metal complexes with proton-coupled electron transfer journal January 2019
Memristive plasticity in artificial electrical synapses via geometrically reconfigurable, gramicidin-doped biomembranes journal January 2019
Memristive Synapses and Neurons for Bioinspired Computing journal November 2018
Threshold Switching in Single Metal‐Oxide Nanobelt Devices Emulating an Artificial Nociceptor journal November 2019
Dynamical nonlinear memory capacitance in biomimetic membranes journal July 2019
Memristive synapses with high reproducibility for flexible neuromorphic networks based on biological nanocomposites journal January 2020
Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges journal September 2019