The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics
Abstract
Solid-state neuromorphic systems based on transistors or memristors have yet to achieve the interconnectivity, performance, and energy efficiency of the brain due to excessive noise, undesirable material properties, and nonbiological switching mechanisms. Here we demonstrate that an alamethicin-doped, synthetic biomembrane exhibits memristive behavior, emulates key synaptic functions including paired-pulse facilitation and depression, and enables learning and computing. Unlike state-of-the-art devices, our two-terminal, biomolecular memristor features similar structure (biomembrane), switching mechanism (ion channels), and ionic transport modality as biological synapses while operating at considerably lower power. The reversible and volatile voltage-driven insertion of alamethicin peptides into an insulating lipid bilayer creates conductive pathways that exhibit pinched current-voltage hysteresis at potentials above their insertion threshold. Moreover, the synapse-like dynamic properties of the biomolecular memristor allow for simplified learning circuit implementations. Low-power memristive devices based on stimuli-responsive biomolecules represent a major advance toward implementation of full synaptic functionality in neuromorphic hardware.
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Cites
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Memristor—The Missing Circuit Element
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Training and operation of an integrated neuromorphic network based on metal-oxide memristors
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The chips are down for Moore’s law
Waldrop, M. M. (2016) · Nature · doi:10.1038/530144a · Cited by 2,397
Neuromorphic electronic systems
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Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials · doi:10.1038/nmat4756 · Cited by 2,358
Atomic structure of conducting nanofilaments in TiO2 resistive switching memory
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Analogue signal and image processing with large memristor crossbars
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Fully memristive neural networks for pattern classification with unsupervised learning
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Cited by
Opportunities for neuromorphic computing algorithms and applications
Schuman, C. D., Kulkarni, S. R., Parsa, M., Mitchell, J. P., Date, P., & Kay, B. (2022) · Nature Computational Science · doi:10.1038/s43588-021-00184-y · Cited by 1,117
Self-citationTang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., et al. (2019) · Advanced Materials · doi:10.1002/adma.201902761 · Cited by 834
An Oxide Schottky Junction Artificial Optoelectronic Synapse
Gao, S., Liu, G., Yang, H., Hu, C., Chen, Q., Gong, G., Xue, W., Yi, X., Shang, J., & Li, R.-W. (2019) · ACS Nano · doi:10.1021/acsnano.9b00340 · Cited by 494
Neuromorphic functions with a polyelectrolyte-confined fluidic memristor
Xiong, T., Li, C., He, X., Xie, B., Zong, J., Jiang, Y., Ma, W., Wu, F., Fei, J., Yu, P., et al. (2023) · Science · doi:10.1126/science.adc9150 · Cited by 386
Metadata
- Authors
- Najem, J. S., Taylor, G. J., Weiss, R. J., Hasan, M. S., Rose, G., Schuman, C. D., Belianinov, A., Collier, C. P., & Sarles, S. A.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-03-26
- Volume
- 12
- Issue
- 5
- Pages
- 4702-4711
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2792860014
- Cited by
- 193
- References
- 18
- FWCI
- 5.17
- Type
- journal article
- Language
- en
- Open access
- closed
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- Najem, J. S., Taylor, G. J., Weiss, R. J., Hasan, M. S., Rose, G., Schuman, C. D., Belianinov, A., Collier, C. P., & Sarles, S. A. (2018). Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics. ACS Nano. https://doi.org/10.1021/acsnano.8b01282
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