Bio-inspired flexible artificial synapses for pain perception and nerve injuries
Source abstract
Abstract
Abstract Imitation of the perception system of living creatures is of great importance for the construction of artificial nerves and intelligent human-machine interfaces. However, a prominent challenge is to emulate the functions of the biological synapse, which is the basic building block of the neural system. Here, inspired by the pain perception mechanism of the living creatures, a flexible double-layer memristor was constructed, with 90% semiconducting single-wall carbon nanotubes (s-SWCNTs) covered by LiClO4doped polyoxyethylene oxide (PEO: LiClO4) as the channel materials. The carriers (protons and Li+) from PEO: LiClO4imitated the functions of Na+and K+in biological systems. A potentiation of the post-synaptic signal was observed with mild stimuli, while the post-synaptic signal was inhibited with severe stimuli with a pulse voltage larger than 1.4 V in this research. These behaviors resemble the sensation of pain, neuroprotection, and possible injuries to the neural system. To explore the underlying mechanism of the phenomenon, the fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), Raman spectrum, and current (IV) sweep were carried out. It was inferred that the observed results are attributable to the interaction between carriers in PEO: LiClO4and functional groups and defects in the s-SWCNTs. The enhanced channel current results from the fulfillment of the traps by the carriers, and the suppression of the current is due to the intercalation of Li+in the s-SWCNTs. This flexible artificial synapse opens a new avenue for the construction of biocompatible electronic devices towards artificial intelligence systems.
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Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Conducting polymers and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
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1. Li, X. Y. & Toyoda, H. Role of leak potassium channels in pain signaling. Brain Res. Bull. 119, 73–79 (2015).10.1016/j.brainresbull.2015.08.007 ↗
2. Galluzzi, K. E. Managing neuropathic pain. J. Am. Ost. Assoc. 107, ES39–ES48 (2007).
3. Waxman, S. G., Dib-Hajj, S., Cummins, T. & Black, J. Sodium channels and pain. Proc. Natl Acad. Sci. 96, 7635–7639 (1999).10.1073/pnas.96.14.7635 ↗
4. Bang, H., Kim, Y. & Kim, D. TREK-2, a new member of the mechanosensitive tandem-pore K+ channel family. J. Biol. Chem. 275, 17412–17419 (2000).10.1074/jbc.M000445200 ↗
5. Sandoz, G., Douguet, D., Chatelain, F., Lazdunski, M. & Lesage, F. Extracellular acidification exerts opposite actions on TREK1 and TREK2 potassium channels via a single conserved histidine residue. Proc. Natl Acad. Sci. 106, 14628–14633 (2009).10.1073/pnas.0906267106 ↗
6. Julius, D. & Basbaum, A. I. Molecular mechanisms of nociception. Nature 413, 203 (2001).10.1038/35093019 ↗
7. Eglen, R. M., Hunter, J. C. & Dray, A. Ions in the fire: recent ion-channel research and approaches to pain therapy. Trends Pharmacol. Sci. 20, 337–342 (1999).10.1016/S0165-6147(99)01372-3 ↗
8. Wood, J. N. et al. Ion channel activities implicated in pathological pain. Novartis Found. Symp. 1999, 32–40 (2004).10.1002/0470869127.ch4 ↗
9. Eijkelkamp, N. et al. Neurological perspectives on voltage-gated sodium channels. Brain 135, 2585–2612 (2012).10.1093/brain/aws225 ↗
10. Mathie, A. & Veale, E. L. Two-pore domain potassium channels: potential therapeutic targets for the treatment of pain. Pflug. Arch. Eur. J. Phy. 467, 931–943 (2015).10.1007/s00424-014-1655-3 ↗
11. Jiang, R., Ma, P., Han, Z. & Du, X. Habituation/Fatigue behavior of a synapse memristor based on IGZO–HfO2 thin film. Sci. Rep. 7, 9354 (2017).10.1038/s41598-017-09762-5 ↗
12. Kim, S. G., Han, J. S., Kim, H., Kim, S. Y. & Jang, H. W. Recent advances in memristive materials for artificial synapses. Adv. Mater. Technol. 3, 1800457 (2018).10.1002/admt.201800457 ↗
13. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101 (2016).10.1038/nmat4756 ↗
14. Diorio, C., Hasler, P., Minch, B. A. & Mead, C. A single-transistor silicon synapse. IEEE Trans. Electr. Dev. 43, 1972–1980 (1996).10.1109/16.543035 ↗
15. Xie, D. et al. Coplanar multigate MoS2 electric-double-layer transistors for neuromorphic visual recognition. ACS Appl. Mater. Inter. 10, 25943–25948.10.1021/acsami.8b07234 ↗
16. Bagdzevicius, S., Maas, K., Boudard, M. & Burriel, M. Interface-type resistive switching in perovskite materials. J. Electroceram. 39, 157–184 (2017).10.1007/s10832-017-0087-9 ↗
17. Yang, J. J. et al. The mechanism of electroforming of metal oxide memristive switches. Nanotechnology 20, 215201 (2009).10.1088/0957-4484/20/21/215201 ↗
18. Sawa, A. Resistive switching in transition metal oxides. Mater. Today 11, 28–36 (2008).10.1016/S1369-7021(08)70119-6 ↗
19. van De Burgt, Y., Melianas, A., Keene, S. T., Malliaras, G. & Salleo, A. Organic electronics for neuromorphic computing. Nat. Electron. 1, 386 (2018).10.1038/s41928-018-0103-3 ↗
20. Huh, W. et al. Synaptic barristor based on phase-engineered 2D heterostructures. Adv. Mater. 30, 1801447 (2018).10.1002/adma.201801447 ↗
21. Qiao, N. et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses. Front. Neurosci. 9, 141 (2015).10.3389/fnins.2015.00141 ↗
22. Furber, S. B., Galluppi, F., Temple, S. & Plana, L. A. The spinnaker project. Proc. IEEE 102, 652–665 (2014).10.1109/JPROC.2014.2304638 ↗
23. Osborn, L., Kaliki, R. R., Soares, A. B. & Thakor, N. V. Neuromimetic event-based detection for closed-loop tactile feedback control of upper limb prostheses. IEEE Trans. Haptics 9, 196–206 (2016).10.1109/TOH.2016.2564965 ↗
24. Beer, R. D., Quinn, R. D., Chiel, H. J. & Ritzmann, R. E. Biologically Inspired. Commun. ACM 40, 31 (1997).10.1145/245108.245118 ↗
25. Goldschmidt, D., Wörgötter, F. & Manoonpong, P. Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots. Front. Neurosci. 8, 3 (2014).
26. Zaghloul, K. A. & Boahen, K. A silicon retina that reproduces signals in the optic nerve. J. Neural Eng. 3, 257 (2006).10.1088/1741-2560/3/4/002 ↗
27. Tee, B. C. K. et al. A skin-inspired organic digital mechanoreceptor. Science 350, 313–316 (2015).10.1126/science.aaa9306 ↗
28. Qiu, S. et al. Solution-processing of high-purity semiconducting single-walled carbon nanotubes for electronics devices. Adv. Mater. 31, 1800750 (2019).10.1002/adma.201800750 ↗
29. Goldstein, S. A., Bockenhauer, D., O’Kelly, I. & Zilberberg, N. Potassium leak channels and the KCNK family of two-P-domain subunits. Nat. Rev. Neurosci. 2, 175 (2001).10.1038/35058574 ↗
30. Qiu, H. et al. Hopping transport through defect-induced localized states in molybdenum disulphide. Nat. Commun. 4, 2642 (2013).10.1038/ncomms3642 ↗
31. Shang, D. S. et al. Effect of carrier trapping on the hysteretic current-voltage characteristics in Ag/La0.7Ca0.3MnO3/Pt heterostructures. Phys. Rev. B 73, 245427 (2006).10.1103/PhysRevB.73.245427 ↗
32. Zhao, M. et al. Curvature-induced condensation of lithium confined inside single-walled carbon nanotubes: first-principles calculations. Phys. Lett. A 340, 434–439 (2005).10.1016/j.physleta.2005.04.026 ↗
33. Zhou, D., Mei, X. & Ouyang, J. Ionic conductivity enhancement of polyethylene oxide-LiClO4 electrolyte by adding functionalized multi-walled carbon nanotubes. J. Phys. Chem. C. 115, 16688–16694 (2011).10.1021/jp203224b ↗
34. Seddon, H. Three types of nerve injury. Brain 66, 237–288 (1943).10.1093/brain/66.4.237 ↗
35. Burnett, M. G. & Zager, E. L. Pathophysiology of peripheral nerve injury: a brief review. Neurosurg. Focus 16, 1–7 (2004).10.3171/foc.2004.16.5.2 ↗