Toward a Hardware Spiking Neural Network: Learning and Adaptation with an Environmental Sustainable Polymer Memristor
Source abstract
Abstract
A hybrid conjugated polymer is presented that supports spike-timing-dependent plasticity (STDP) for neuromorphic computing devices while enabling environmental sustainability through biodegradability. The polymer, distinguished by its carbazole backbone and electrically responsive pendant carbazole groups, forms a two-terminal device that exhibits analog STDP behavior. Demonstrations focus on fundamental switching characteristics using individual devices, offering insights relevant to temporal learning tasks such as speech and image recognition. The energy cost per programming event is 60 nJ. Biodegradation is demonstrated using Pseudomonas resinovorans CA10 lysate, supporting reduced electronic waste. This work introduces a sustainable soft-matter platform for synaptic device development.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Photoreceptor and optogenetics research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Materials Science and Engineering
Clemson University
Department of Bioengineering
Center for Optical Materials Science and Engineering Technologies (COMSET)
Department of Genetics and Biochemistry
Institute of Physiology, Computational Neuroscience Group, Czech Academy of Sciences, Videnska 1083, 142 20 Prague 4, Czech Republic
Otto Wichterle Centre of Polymer Materials and Technologies
Institute of Macromolecular Chemistry
Department of Physics and Materials Engineering, Faculty of Technology, Tomas Bata University in Zlín, Tř. T. Bati 5678, 760 01 Zlín, Czech Republic
Funding
Ministerstvo ?kolstv?, Ml?de?e a Telov?chovy · Office of Advanced Cyberinfrastructure · Gregg-Graniteville Foundation
References (63)
Show source-provided references
1. 10.1038/s41586-019-1677-210.1038/s41586-019-1677-2 ↗
2. 10.1109/JPROC.2014.230463810.1109/JPROC.2014.2304638 ↗
3. 10.1021/nl904092h10.1021/nl904092h ↗
4. 10.1109/TCAD.2015.247439610.1109/TCAD.2015.2474396 ↗
5. 10.1109/MM.2018.11213035910.1109/MM.2018.112130359 ↗
6. 10.1002/adma.20220504710.1002/adma.202205047 ↗
7. 10.1002/aelm.20220083310.1002/aelm.202200833 ↗
8. 10.1039/C4MH00067F10.1039/C4MH00067F ↗
9. 10.1002/aelm.20160051010.1002/aelm.201600510 ↗
10. 10.1002/aelm.20190019710.1002/aelm.201900197 ↗
11. 10.1103/RevModPhys.45.57410.1103/RevModPhys.45.574 ↗
12. 10.1080/1364281860823900910.1080/13642818608239009 ↗
13. 10.1002/(SICI)1099-0488(19990801)37:15<1899::AID-POLB14>3.0.CO;2-010.1002/(SICI)1099-0488(19990801)37:15<1899::AID-POLB14>3.0.CO;2-0 ↗
14. 10.1039/D1TC00987G10.1039/D1TC00987G ↗
15. 10.1021/ma061152310.1021/ma0611523 ↗
16. 10.1021/am800089n10.1021/am800089n ↗
17. 10.1021/cm071520x10.1021/cm071520x ↗
18. 10.1039/c0jm02535f10.1039/c0jm02535f ↗
19. 10.1039/c0jm04344c10.1039/c0jm04344c ↗
20. 10.1039/C7TC05001A10.1039/C7TC05001A ↗
21. 10.1002/aelm.20200004210.1002/aelm.202000042 ↗
22. 10.1098/rsta.2008.026210.1098/rsta.2008.0262 ↗
23. 10.1002/chem.20110080710.1002/chem.201100807 ↗
24. 10.1021/jp101062a10.1021/jp101062a ↗
25. 10.1002/adma.20110364710.1002/adma.201103647 ↗
26. 10.1002/adfm.20120007310.1002/adfm.201200073 ↗
27. 10.1021/acsnano.7b0334710.1021/acsnano.7b03347 ↗
28. 10.1002/smll.20170239010.1002/smll.201702390 ↗
29. 10.1016/j.chemphys.2018.09.00810.1016/j.chemphys.2018.09.008 ↗
30. 10.1039/C8NR07442A10.1039/C8NR07442A ↗
31. 10.1002/aisy.20220027210.1002/aisy.202200272 ↗
32. 10.1002/adfm.20110068610.1002/adfm.201100686 ↗
33. 10.1109/TCT.1971.108333710.1109/TCT.1971.1083337 ↗
34. 10.1088/0268-1242/29/10/10400110.1088/0268-1242/29/10/104001 ↗
35. 10.3390/polym901002510.3390/polym9010025 ↗
36. 10.1002/pol.1985.13023030610.1002/pol.1985.130230306 ↗
37. 10.1063/1.36898110.1063/1.368981 ↗
38. 10.1002/anie.20050436510.1002/anie.200504365 ↗
39. Gerstner W. · 201410.1017/CBO9781107447615 ↗
40. 10.1038/8145310.1038/81453 ↗
41. 10.1038/7882910.1038/78829 ↗
42. 10.1007/BF0019945010.1007/BF00199450 ↗
43. 10.1523/JNEUROSCI.6541-10.201110.1523/JNEUROSCI.6541-10.2011 ↗
44. 10.1038/383076a010.1038/383076a0 ↗
45. 10.1103/PhysRevE.59.449810.1103/PhysRevE.59.4498 ↗
46. 10.1038/nature0301010.1038/nature03010 ↗
47. 10.1523/JNEUROSCI.18-24-10464.199810.1523/JNEUROSCI.18-24-10464.1998 ↗
48. 10.1126/science.125464210.1126/science.1254642 ↗
49. 10.3389/fnins.2013.0000210.3389/fnins.2013.00002 ↗
50. 10.1109/JPROC.2015.244409410.1109/JPROC.2015.2444094 ↗
51. 10.1039/D1NH00481F10.1039/D1NH00481F ↗
52. 10.1039/D1NA00703C10.1039/D1NA00703C ↗
53. 10.1146/annurev.neuro.29.051605.11281910.1146/annurev.neuro.29.051605.112819 ↗
54. 10.1021/acscentsci.7b0059510.1021/acscentsci.7b00595 ↗
55. 10.1271/bbb.57.45510.1271/bbb.57.455 ↗
56. 10.1128/JB.181.10.3105-3113.199910.1128/JB.181.10.3105-3113.1999 ↗
57. 10.1128/jb.179.15.4841-4849.199710.1128/jb.179.15.4841-4849.1997 ↗
58. 10.2323/jgam.47.27910.2323/jgam.47.279 ↗
59. 10.1111/j.1751-1097.2011.01023.x10.1111/j.1751-1097.2011.01023.x ↗
60. 10.1002/smll.20120001310.1002/smll.201200013 ↗
61. 10.1016/j.chemosphere.2019.12577010.1016/j.chemosphere.2019.125770 ↗
62. 10.1021/la202122p10.1021/la202122p ↗
63. 10.1021/om100202c10.1021/om100202c ↗