skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Biomimetic, Soft-Material Synapse for Neuromorphic Computing: from Device to Network

Abstract

Neuromorphic computing refers to a variety of brain-inspired computers, devices, and models inspired by the interconnectivity, performance, and energy efficiency of the human brain. Unlike the ubiquitous von Neumann computer architectures with complex processor cores and sequential computation, biological neurons and synapses operate by storing and processing information simultaneously with the capacity of flexible adaptation resulting in massive computational capability with much less power consumption. The search for a synaptic material which can closely imitate bio-synapse has led to an alamethicin-doped, synthetic biomembrane which can emulate key synaptic functions due to generic memristive property enabling learning and computation. This two-terminal, biomolecular memristor, in contrast to its solid-state counterparts, features similar structure, switching mechanism, and ionic transport modality as biological synapses while consuming considerably lower power. In this paper, we outline a methodology for using this biomolecular synapse to build neural networks capable of solving real-world problems. The physical mechanism underlying its volatile memristance is explored followed by the development of a model of this device for circuit simulation. We outline a circuit design technique to integrate this synapse with solid-state neuron circuit for hardware implementation. Based on these results, we develop a high level simulation framework and use a trainingmore » scheme called Evolutionary Optimization for Neuromorphic System (EONS) to generate networks for solving two problems, namely iris dataset classification and EEG classification task. The small network size and comparable to state-of-the-art accuracy of these preliminary networks show its potential to enhance synaptic functionality in next generation neuromorphic hardware.« less

Authors:
 [1]; ORCiD logo [2];  [2];  [1];  [3]; ORCiD logo [2]; ORCiD logo [2];  [1];  [3]
  1. The University of Tennessee, Knoxville
  2. ORNL
  3. University of Tennessee (UT)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1492161
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 13th IEEE Dallas Circuits and Systems Conference (DCAS 2018) - Dallas, Texas, United States of America - 11/12/2018 10:00:00 AM-11/12/2018 10:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Sakib hasan, Md, Schuman, Catherine D., Najem, Joseph S., Weiss, Ryan, Skuda, Nicholas, Belianinov, Alex, Collier, Pat, Sarles, Stephen, and Rose, Garrett. Biomimetic, Soft-Material Synapse for Neuromorphic Computing: from Device to Network. United States: N. p., 2019. Web.
Sakib hasan, Md, Schuman, Catherine D., Najem, Joseph S., Weiss, Ryan, Skuda, Nicholas, Belianinov, Alex, Collier, Pat, Sarles, Stephen, & Rose, Garrett. Biomimetic, Soft-Material Synapse for Neuromorphic Computing: from Device to Network. United States.
Sakib hasan, Md, Schuman, Catherine D., Najem, Joseph S., Weiss, Ryan, Skuda, Nicholas, Belianinov, Alex, Collier, Pat, Sarles, Stephen, and Rose, Garrett. Tue . "Biomimetic, Soft-Material Synapse for Neuromorphic Computing: from Device to Network". United States. https://www.osti.gov/servlets/purl/1492161.
@article{osti_1492161,
title = {Biomimetic, Soft-Material Synapse for Neuromorphic Computing: from Device to Network},
author = {Sakib hasan, Md and Schuman, Catherine D. and Najem, Joseph S. and Weiss, Ryan and Skuda, Nicholas and Belianinov, Alex and Collier, Pat and Sarles, Stephen and Rose, Garrett},
abstractNote = {Neuromorphic computing refers to a variety of brain-inspired computers, devices, and models inspired by the interconnectivity, performance, and energy efficiency of the human brain. Unlike the ubiquitous von Neumann computer architectures with complex processor cores and sequential computation, biological neurons and synapses operate by storing and processing information simultaneously with the capacity of flexible adaptation resulting in massive computational capability with much less power consumption. The search for a synaptic material which can closely imitate bio-synapse has led to an alamethicin-doped, synthetic biomembrane which can emulate key synaptic functions due to generic memristive property enabling learning and computation. This two-terminal, biomolecular memristor, in contrast to its solid-state counterparts, features similar structure, switching mechanism, and ionic transport modality as biological synapses while consuming considerably lower power. In this paper, we outline a methodology for using this biomolecular synapse to build neural networks capable of solving real-world problems. The physical mechanism underlying its volatile memristance is explored followed by the development of a model of this device for circuit simulation. We outline a circuit design technique to integrate this synapse with solid-state neuron circuit for hardware implementation. Based on these results, we develop a high level simulation framework and use a training scheme called Evolutionary Optimization for Neuromorphic System (EONS) to generate networks for solving two problems, namely iris dataset classification and EEG classification task. The small network size and comparable to state-of-the-art accuracy of these preliminary networks show its potential to enhance synaptic functionality in next generation neuromorphic hardware.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: