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Armendarez NX, Mohamed AS, Dhungel A, Hossain MR, Hasan MS, Najem JS. Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity. ACS APPLIED MATERIALS & INTERFACES 2024; 16:6176-6188. [PMID: 38271202 DOI: 10.1021/acsami.3c16003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10-3, using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.
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Affiliation(s)
- Nicholas X Armendarez
- Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States
| | - Ahmed S Mohamed
- Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States
| | - Anurag Dhungel
- Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States
| | - Md Razuan Hossain
- Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States
| | - Md Sakib Hasan
- Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States
| | - Joseph S Najem
- Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States
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Bolmatov D, Collier CP, Zav’yalov D, Egami T, Katsaras J. Real Space and Time Imaging of Collective Headgroup Dipole Motions in Zwitterionic Lipid Bilayers. MEMBRANES 2023; 13:442. [PMID: 37103869 PMCID: PMC10142431 DOI: 10.3390/membranes13040442] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/05/2023] [Accepted: 04/14/2023] [Indexed: 06/19/2023]
Abstract
Lipid bilayers are supramolecular structures responsible for a range of processes, such as transmembrane transport of ions and solutes, and sorting and replication of genetic materials, to name just a few. Some of these processes are transient and currently, cannot be visualized in real space and time. Here, we developed an approach using 1D, 2D, and 3D Van Hove correlation functions to image collective headgroup dipole motions in zwitterionic phospholipid bilayers. We show that both 2D and 3D spatiotemporal images of headgroup dipoles are consistent with commonly understood dynamic features of fluids. However, analysis of the 1D Van Hove function reveals lateral transient and re-emergent collective dynamics of the headgroup dipoles-occurring at picosecond time scales-that transmit and dissipate heat at longer times, due to relaxation processes. At the same time, the headgroup dipoles also generate membrane surface undulations due a collective tilting of the headgroup dipoles. A continuous intensity band of headgroup dipole spatiotemporal correlations-at nanometer length and nanosecond time scales-indicates that dipoles undergo stretching and squeezing elastic deformations. Importantly, the above mentioned intrinsic headgroup dipole motions can be externally stimulated at GHz-frequency scale, enhancing their flexoelectric and piezoelectric capabilities (i.e., increased conversion efficiency of mechanical energy into electric energy). In conclusion, we discuss how lipid membranes can provide molecular-level insights about biological learning and memory, and as platforms for the development of the next generation of neuromorphic computers.
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Affiliation(s)
- Dima Bolmatov
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA
- Shull-Wollan Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - C. Patrick Collier
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Dmitry Zav’yalov
- Department of Physics, Volgograd State Technical University, Volgograd 400005, Russia
| | - Takeshi Egami
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA
- Shull-Wollan Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Department of Materials Science and Engineering, The University of Tennessee, Knoxville, TN 37916, USA
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - John Katsaras
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA
- Shull-Wollan Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Sample Environment Group, Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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Fu T, Fu S, Yao J. Recent progress in bio-voltage memristors working with ultralow voltage of biological amplitude. NANOSCALE 2023; 15:4669-4681. [PMID: 36779566 DOI: 10.1039/d2nr06773k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Neuromorphic systems built from memristors that emulate bioelectrical information processing in the brain may overcome the limitations of traditional computing architectures. However, functional emulation alone may still not attain all the merits of bio-computation, which uses action potentials of 50-120 mV at least 10 times lower than signal amplitude in conventional electronics to achieve extraordinary power efficiency and effective functional integration. Reducing the functional voltage in memristors to this biological amplitude can thus advance neuromorphic engineering and bio-emulated integration. This review aims to provide a timely update on the effort and progress in this burgeoning research direction, covering the aspects of device material composition, performance, working mechanism, and potential application.
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Affiliation(s)
- Tianda Fu
- Department of Electrical Computer and Engineering, University of Massachusetts, Amherst, MA 01003, USA.
| | - Shuai Fu
- Institute for Applied Life Sciences (IALS), University of Massachusetts, Amherst, MA 01003, USA
| | - Jun Yao
- Department of Electrical Computer and Engineering, University of Massachusetts, Amherst, MA 01003, USA.
- Institute for Applied Life Sciences (IALS), University of Massachusetts, Amherst, MA 01003, USA
- Department of Biomedical Engineering, University of Massachusetts, Amherst, MA 01003, USA
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