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Title: Response of a Memristive Biomembrane and Demonstration of Potential Use in Online Learning

Abstract

The pervasive von Neumann architecture uses complex processor cores and sequential computation. In contrast, the brain is massively parallel and highly efficient, owing to the ability of the neurons and synapses to store and process information simultaneously and to adapt according to incoming information. These features have motivated researchers to develop a host of brain-inspired computers, devices, and models, collectively referred to as neuromorphic computing systems. The quest for synaptic materials capable of closely mimicking biological synapses has led to an alamethicin-doped, synthetic biomembrane with volatile memristive properties which can emulate key synaptic functions to facilitate learning and computation. In contrast to its solid-state counterparts, this two-terminal, biomolecular memristor features similar structure, switching mechanisms, and ionic transport modality as biological synapses while consuming considerably lower power. To use the device as a circuit element, it is important to understand its response to different kinds of input signals. Here we develop a simplified closed form analytical solution based on the underlying state equations for pulse and sine wave inputs. A Verilog-A model based on Runge-Kutta method was developed to incorporate the device in a circuit simulator. Finally, the paper demonstrates possible applications for short- and long-term learning using its unique volatilemore » memristive properties.« less

Authors:
 [1];  [2];  [3]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2];  [3];  [4]
  1. The University of Tennessee Knoxville
  2. ORNL
  3. The University of Tennessee, Knoxville
  4. University of Tennessee (UT)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1491303
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE 13th Nanotechnology Materials and Devices Conference (NMDC) - Portland, Oregon, United States of America - 10/14/2018 4:00:00 AM-10/17/2018 4:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Hasan, Md Sakib, Najem, Joseph S., Weiss, Ryan, Schuman, Catherine D., Belianinov, Alex, Collier, Pat, Sarles, Stephen, and Rose, Garrett. Response of a Memristive Biomembrane and Demonstration of Potential Use in Online Learning. United States: N. p., 2019. Web.
Hasan, Md Sakib, Najem, Joseph S., Weiss, Ryan, Schuman, Catherine D., Belianinov, Alex, Collier, Pat, Sarles, Stephen, & Rose, Garrett. Response of a Memristive Biomembrane and Demonstration of Potential Use in Online Learning. United States.
Hasan, Md Sakib, Najem, Joseph S., Weiss, Ryan, Schuman, Catherine D., Belianinov, Alex, Collier, Pat, Sarles, Stephen, and Rose, Garrett. Tue . "Response of a Memristive Biomembrane and Demonstration of Potential Use in Online Learning". United States. https://www.osti.gov/servlets/purl/1491303.
@article{osti_1491303,
title = {Response of a Memristive Biomembrane and Demonstration of Potential Use in Online Learning},
author = {Hasan, Md Sakib and Najem, Joseph S. and Weiss, Ryan and Schuman, Catherine D. and Belianinov, Alex and Collier, Pat and Sarles, Stephen and Rose, Garrett},
abstractNote = {The pervasive von Neumann architecture uses complex processor cores and sequential computation. In contrast, the brain is massively parallel and highly efficient, owing to the ability of the neurons and synapses to store and process information simultaneously and to adapt according to incoming information. These features have motivated researchers to develop a host of brain-inspired computers, devices, and models, collectively referred to as neuromorphic computing systems. The quest for synaptic materials capable of closely mimicking biological synapses has led to an alamethicin-doped, synthetic biomembrane with volatile memristive properties which can emulate key synaptic functions to facilitate learning and computation. In contrast to its solid-state counterparts, this two-terminal, biomolecular memristor features similar structure, switching mechanisms, and ionic transport modality as biological synapses while consuming considerably lower power. To use the device as a circuit element, it is important to understand its response to different kinds of input signals. Here we develop a simplified closed form analytical solution based on the underlying state equations for pulse and sine wave inputs. A Verilog-A model based on Runge-Kutta method was developed to incorporate the device in a circuit simulator. Finally, the paper demonstrates possible applications for short- and long-term learning using its unique volatile memristive properties.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Conference:
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