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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
The chips are down for Moore’s law
Waldrop, M. M. (2016) · Nature · doi:10.1038/530144a · Cited by 2,397
Neuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368
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
Kwon, D.-H., Kim, K. M., Jang, J. H., Jeon, J. M., Lee, M. H., Kim, G. H., Li, X.-S., Park, G.-S., Lee, B., Han, S., et al. (2010) · Nature Nanotechnology · doi:10.1038/nnano.2009.456 · Cited by 2,092
Analogue signal and image processing with large memristor crossbars
Li, C., Hu, M., Li, Y., Jiang, H., Ge, N., Montgomery, E., Zhang, J., Song, W., Dávila, N., Graves, C. E., et al. (2017) · Nature Electronics · doi:10.1038/s41928-017-0002-z · Cited by 1,292
Fully memristive neural networks for pattern classification with unsupervised learning
Wang, Z., Joshi, S., Savel’ev, S., Song, W., Midya, R., Li, Y., Rao, M., Yan, P., Asapu, S., Zhuo, Y., et al. (2018) · Nature Electronics · doi:10.1038/s41928-018-0023-2 · Cited by 1,106
Efficiency of Functional Brain Networks and Intellectual Performance
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1443-09.2009 · Cited by 1,078
How We Found The Missing Memristor
Williams, R. (2008) · IEEE Spectrum · doi:10.1109/mspec.2008.4687366 · Cited by 697
If it's pinched it's a memristor
Chua, L. (2014) · Semiconductor Science and Technology · doi:10.1088/0268-1242/29/10/104001 · Cited by 685
Kim, S., Du, C., Sheridan, P., Ma, W., Choi, S., & Lu, W. D. (2015) · Nano Letters · doi:10.1021/acs.nanolett.5b00697 · Cited by 603
Du, C., Ma, W., Chang, T., Sheridan, P., & Lu, W. D. (2015) · Advanced Functional Materials · doi:10.1002/adfm.201501427 · Cited by 444
A single-transistor silicon synapse
Diorio, C., Hasler, P., Minch, A., & Mead, C. A. (1996) · IEEE Transactions on Electron Devices · doi:10.1109/16.543035 · Cited by 360
HODGKIN–HUXLEY AXON IS MADE OF MEMRISTORS
CHUA, L., SBITNEV, V., & KIM, H. (2012) · International Journal of Bifurcation and Chaos · doi:10.1142/s021812741230011x · Cited by 319
From the stochasticity of molecular processes to the variability of synaptic transmission
Ribrault, C., Sekimoto, K., & Triller, A. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3025 · Cited by 198
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
