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Title: A Soft-Matter Biomolecular Memristor Synapse for Neuromorphic Systems

Abstract

The goal of neuromorphic computing is to recreate the computational power and efficiency of the human brain with circuitry. The ability of the brain to solve complex real time tasks, while consuming 20 W of power on average, is made possible through its connection density, adaptability, and parallel processing. Recreating these features using traditional electronics circuit elements is incredibly difficult, and therefore, soft-matter memristors made of biomolecules similar to those found in biological synapses and capable of emulating various synaptic features can be used as neuromorphic hardware. In this work, we introduce and experimentally demonstrate an electronic neuron circuit capable of interacting with ionic, soft-matter memristors. These memristors are proven to exhibit short-term plasticity, especially paired-pulse facilitation and depression found in presynaptic terminals - features that are not found in state-of-the-art solid-state memristors. We make use of these features for applications in online learning by developing a synapse-neuron circuit which implements spike-rate-dependent plasticity (SRDP) as a learning function.

Authors:
 [1];  [2];  [3]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2];  [1];  [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:
1489557
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) - Cleveland, Ohio, United States of America - 10/17/2018 4:00:00 AM-10/19/2018 4:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Weiss, Ryan, Najem, Joseph S., Hasan, Md Sakib, Schuman, Catherine D., Belianinov, Alex, Collier, Pat, Sarles, Stephen, and Rose, Garrett. A Soft-Matter Biomolecular Memristor Synapse for Neuromorphic Systems. United States: N. p., 2018. Web. doi:10.1109/BIOCAS.2018.8584668.
Weiss, Ryan, Najem, Joseph S., Hasan, Md Sakib, Schuman, Catherine D., Belianinov, Alex, Collier, Pat, Sarles, Stephen, & Rose, Garrett. A Soft-Matter Biomolecular Memristor Synapse for Neuromorphic Systems. United States. doi:10.1109/BIOCAS.2018.8584668.
Weiss, Ryan, Najem, Joseph S., Hasan, Md Sakib, Schuman, Catherine D., Belianinov, Alex, Collier, Pat, Sarles, Stephen, and Rose, Garrett. Sat . "A Soft-Matter Biomolecular Memristor Synapse for Neuromorphic Systems". United States. doi:10.1109/BIOCAS.2018.8584668. https://www.osti.gov/servlets/purl/1489557.
@article{osti_1489557,
title = {A Soft-Matter Biomolecular Memristor Synapse for Neuromorphic Systems},
author = {Weiss, Ryan and Najem, Joseph S. and Hasan, Md Sakib and Schuman, Catherine D. and Belianinov, Alex and Collier, Pat and Sarles, Stephen and Rose, Garrett},
abstractNote = {The goal of neuromorphic computing is to recreate the computational power and efficiency of the human brain with circuitry. The ability of the brain to solve complex real time tasks, while consuming 20 W of power on average, is made possible through its connection density, adaptability, and parallel processing. Recreating these features using traditional electronics circuit elements is incredibly difficult, and therefore, soft-matter memristors made of biomolecules similar to those found in biological synapses and capable of emulating various synaptic features can be used as neuromorphic hardware. In this work, we introduce and experimentally demonstrate an electronic neuron circuit capable of interacting with ionic, soft-matter memristors. These memristors are proven to exhibit short-term plasticity, especially paired-pulse facilitation and depression found in presynaptic terminals - features that are not found in state-of-the-art solid-state memristors. We make use of these features for applications in online learning by developing a synapse-neuron circuit which implements spike-rate-dependent plasticity (SRDP) as a learning function.},
doi = {10.1109/BIOCAS.2018.8584668},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {12}
}

Conference:
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