Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision
Source abstract
Abstract
Abstract The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal‐oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy‐efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware‐implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next‐generation artificial visual perception systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · CCD and CMOS Imaging Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Microscale Optoelectronics Shenzhen University Shenzhen 518060 P. R. China
College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 P. R. China
Institute for Advanced Study Shenzhen University Shenzhen 518060 P. R. China
School of Science and Technology Hong Kong Metropolitan University Hong Kong 999077 P. R. China
References (100+)
Show source-provided references
1. 10.1038/s41467-021-21404-z10.1038/s41467-021-21404-z ↗
2. 10.1038/s41928-020-0422-z10.1038/s41928-020-0422-z ↗
3. 10.1002/aisy.20190000310.1002/aisy.201900003 ↗
4. 10.1109/JSSC.2021.306176010.1109/JSSC.2021.3061760 ↗
5. 10.1016/j.matt.2021.02.01810.1016/j.matt.2021.02.018 ↗
6. 10.1109/LED.2021.307393010.1109/LED.2021.3073930 ↗
7. J.Huang M.Guo S.Wang S.Chen in2018 IEEE Int. Symp. Circuits Syst. ISCAS IEEE Florence2018.
8. 10.1038/s41586-020-2038-x10.1038/s41586-020-2038-x ↗
9. 10.1038/s41467-020-18375-y10.1038/s41467-020-18375-y ↗
10. 10.1021/acs.chemrev.9b0073010.1021/acs.chemrev.9b00730 ↗
11. 10.1021/acsnano.2c0290610.1021/acsnano.2c02906 ↗
12. 10.1038/s41928-022-00789-910.1038/s41928-022-00789-9 ↗
13. 10.1038/s41928-020-0429-510.1038/s41928-020-0429-5 ↗
14. 10.1016/j.neuron.2012.10.00210.1016/j.neuron.2012.10.002 ↗
15. 10.1038/s41467-020-19806-610.1038/s41467-020-19806-6 ↗
16. 10.1038/s41586-020-2285-x10.1038/s41586-020-2285-x ↗
17. 10.1002/advs.20210463210.1002/advs.202104632 ↗
18. 10.1016/S0042-6989(00)00166-810.1016/S0042-6989(00)00166-8 ↗
19. 10.1113/jphysiol.1965.sp00763810.1113/jphysiol.1965.sp007638 ↗
20. 10.1039/c3tb20213e10.1039/c3tb20213e ↗
21. 10.1002/adma.20190689910.1002/adma.201906899 ↗
22. 10.1002/aisy.20200022410.1002/aisy.202000224 ↗
23. 10.1021/acsnano.2c0103510.1021/acsnano.2c01035 ↗
24. 10.1002/adma.20200243110.1002/adma.202002431 ↗
25. 10.1002/adma.20210897910.1002/adma.202108979 ↗
26. 10.1002/aisy.20220001910.1002/aisy.202200019 ↗
27. 10.1002/aisy.20210005410.1002/aisy.202100054 ↗
28. 10.1038/s41467-023-36270-010.1038/s41467-023-36270-0 ↗
29. 10.1002/aisy.20210025710.1002/aisy.202100257 ↗
30. 10.1002/adma.20190243410.1002/adma.201902434 ↗
31. 10.1002/adma.20190643310.1002/adma.201906433 ↗
32. 10.1002/adma.20190309510.1002/adma.201903095 ↗
33. 10.1038/nrn149710.1038/nrn1497 ↗
34. 10.1038/nn0901-87710.1038/nn0901-877 ↗
35. 10.1016/j.tins.2013.04.00610.1016/j.tins.2013.04.006 ↗
36. 10.1038/s41928-022-00713-110.1038/s41928-022-00713-1 ↗
37. 10.1016/j.preteyeres.2009.05.00310.1016/j.preteyeres.2009.05.003 ↗
38. 10.1038/scientificamerican0487-4010.1038/scientificamerican0487-40 ↗
39. 10.1113/jphysiol.1980.sp01309710.1113/jphysiol.1980.sp013097 ↗
40. 10.1038/eye.2001.14110.1038/eye.2001.141 ↗
41. 10.1177/153537021558753010.1177/1535370215587530 ↗
42. 10.1126/sciadv.aba617310.1126/sciadv.aba6173 ↗
43. 10.1038/nrn378310.1038/nrn3783 ↗
44. 10.1113/JP27417710.1113/JP274177 ↗
45. 10.1017/S095252381100036810.1017/S0952523811000368 ↗
46. 10.1016/S0896-6273(00)80478-X10.1016/S0896-6273(00)80478-X ↗
47. 10.1016/0042-6989(81)90013-410.1016/0042-6989(81)90013-4 ↗
48. 10.1017/S095252381100034410.1017/S0952523811000344 ↗
49. 10.1016/S1350-9462(00)00024-010.1016/S1350-9462(00)00024-0 ↗
50. 10.1016/j.conb.2013.08.00610.1016/j.conb.2013.08.006 ↗
51. 10.1016/j.preteyeres.2012.03.00310.1016/j.preteyeres.2012.03.003 ↗
52. 10.1109/TPAMI.2012.27210.1109/TPAMI.2012.272 ↗
53. 10.1038/nrn105510.1038/nrn1055 ↗
54. 10.1113/jphysiol.2009.17070410.1113/jphysiol.2009.170704 ↗
55. 10.1016/j.visres.2011.02.01210.1016/j.visres.2011.02.012 ↗
56. 10.1038/1119710.1038/11197 ↗
57. 10.1152/jn.00692.200110.1152/jn.00692.2001 ↗
58. 10.1016/j.neuron.2008.10.00810.1016/j.neuron.2008.10.008 ↗
59. 10.1016/j.preteyeres.2020.10088610.1016/j.preteyeres.2020.100886 ↗
60. 10.1126/science.328393610.1126/science.3283936 ↗
61. 10.1073/pnas.161007811310.1073/pnas.1610078113 ↗
62. 10.1126/science.aaa910110.1126/science.aaa9101 ↗
63. 10.1523/JNEUROSCI.4348-10.201010.1523/JNEUROSCI.4348-10.2010 ↗
64. 10.1038/nn203910.1038/nn2039 ↗
65. 10.1113/jphysiol.1978.sp01226910.1113/jphysiol.1978.sp012269 ↗
66. 10.1523/JNEUROSCI.0025-16.201610.1523/JNEUROSCI.0025-16.2016 ↗
67. 10.1016/j.pneurobio.2022.10237410.1016/j.pneurobio.2022.102374 ↗
68. 10.1016/S0896-6273(01)00313-010.1016/S0896-6273(01)00313-0 ↗
69. 10.1152/jn.1983.49.5.112710.1152/jn.1983.49.5.1127 ↗
70. 10.1007/BF0096187610.1007/BF00961876 ↗
71. 10.1152/physrev.00008.200710.1152/physrev.00008.2007 ↗
72. 10.1016/j.brainresrev.2009.10.00110.1016/j.brainresrev.2009.10.001 ↗
73. 10.1093/cercor/bhh00110.1093/cercor/bhh001 ↗
74. 10.1016/j.conb.2019.06.00110.1016/j.conb.2019.06.001 ↗
75. 10.1002/advs.20220201910.1002/advs.202202019 ↗
76. 10.1002/adma.20220189510.1002/adma.202201895 ↗
77. 10.1109/JSEN.2016.256263810.1109/JSEN.2016.2562638 ↗
78. 10.1109/TED.2019.290419010.1109/TED.2019.2904190 ↗
79. 10.1109/TBCAS.2017.275978310.1109/TBCAS.2017.2759783 ↗
80. 10.1002/adma.20210011910.1002/adma.202100119 ↗
81. J.‐K.Han J.Sim D.‐M.Geum S. K.Kim J.‐M.Yu J.Kim S.Kim Y.‐K.Choi in2021 IEEE Int. Electron Devices Meet. IEDM IEEE Piscataway NJ 2021.
82. 10.1002/adma.20220849710.1002/adma.202208497 ↗
83. 10.1038/nrn288010.1038/nrn2880 ↗
84. 10.1037/11304-02610.1037/11304-026 ↗
85. 10.1002/adma.20200600610.1002/adma.202006006 ↗
86. 10.1002/adma.20170540010.1002/adma.201705400 ↗
87. 10.1002/adma.20170177210.1002/adma.201701772 ↗
88. 10.1126/sciadv.aat738710.1126/sciadv.aat7387 ↗
89. 10.1016/j.nanoen.2020.10524610.1016/j.nanoen.2020.105246 ↗
90. 10.1146/annurev.physiol.64.092501.11454710.1146/annurev.physiol.64.092501.114547 ↗
91. 10.1242/jeb.203.23.359510.1242/jeb.203.23.3595 ↗
92. Lynch M. A. · Physiol. Rev. · 2004 · 84
93. 10.1073/pnas.90.8.312110.1073/pnas.90.8.3121 ↗
94. 10.1038/nn138710.1038/nn1387 ↗
95. 10.1038/8145310.1038/81453 ↗
96. 10.1016/j.neuron.2004.12.04510.1016/j.neuron.2004.12.045 ↗
97. 10.1016/j.matt.2022.02.02010.1016/j.matt.2022.02.020 ↗
98. 10.1002/advs.20210609210.1002/advs.202106092 ↗
99. 10.1038/s41565-019-0501-310.1038/s41565-019-0501-3 ↗
100. 10.1002/adma.20210298010.1002/adma.202102980 ↗