Abstract
Memristors are solid-state devices that exhibit voltagecontrolled conductance. This tunable functionality enables the implementation of biologically-inspired synaptic functions in solid-state neuromorphic computing systems. However, while memristors are meant to emulate an intricate signal transduction process performed by soft biomolecular structures, they are commonly constructed from silicon- or polymer-based materials. As a result, the volatility, intricate design, and high-energy resistance switching in memristive devices, usually, leads to energy consumption in memristors that is several orders of magnitude higher than in natural synapses. Additionally, solidstate memristors fail to achieve the coupled dynamics and selectivity of synaptic ion exchange that are believed to be necessary for initiating both short- and long-term potentiation (STP and LTP) in neural synapses, as well as paired-pulse facilitation (PPF) in the presynaptic terminal. LTP is a phenomenon mostly responsible for driving synaptic learning and memory, features that enable signal transduction between neurons to be history-dependent and adaptable. In contrast, current memristive devices rely on engineered external programming parameters to imitate LTP. Because of these.
Original language | English |
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Title of host publication | Development and Characterization of Multifunctional Materials; Mechanics and Behavior of Active Materials; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies |
Publisher | American Society of Mechanical Engineers |
ISBN (Electronic) | 9780791858257 |
DOIs | |
State | Published - 2017 |
Event | ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2017 - Snowbird, United States Duration: Sep 18 2017 → Sep 20 2017 |
Publication series
Name | ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2017 |
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Volume | 1 |
Conference
Conference | ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2017 |
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Country/Territory | United States |
City | Snowbird |
Period | 09/18/17 → 09/20/17 |
Funding
1 Notice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. We would like to acknowledge the financial support provided by the National Science Foundation Grant NSF ECCS-1631472.