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Libros sobre el tema "Neuromorphic applications"

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Autor: Grafiati
Publicado: 6 de septiembre de 2023
Última modificación: 7 de julio de 2024

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1

Kozma, Robert, Robinson E. Pino y Giovanni E. Pazienza, eds. Advances in Neuromorphic Memristor Science and Applications. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4491-2.

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2

Kozma, Robert. Advances in Neuromorphic Memristor Science and Applications. Dordrecht: Springer Netherlands, 2012.

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3

Beaton, Paul Timothy, ed. Frontiers in Memristive Materials for Neuromorphic Processing Applications. Washington, D.C.: National Academies Press, 2020. http://dx.doi.org/10.17226/25938.

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4

C, Merrill Walter y United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Neuromorphic learning of continuous-valued mappings from noise-corrupted data: Application to real-time adaptive control. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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5

Bartolozzi, Chiara, Emre O. Neftci y Elisabetta Chicca, eds. Neuromorphic Engineering Systems and Applications. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-723-1.

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6

van Schaik, André, Tobi Delbruck y Jennifer Hasler, eds. Neuromorphic Engineering Systems and Applications. Frontiers Media SA, 2015. http://dx.doi.org/10.3389/978-2-88919-454-4.

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7

Dong, Yibo, Min Gu, Elena Goi, Yangyundou Wang y Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.

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8

Wang, Jing, Min Gu, Elena Goi, Yangyundou Wang y Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.

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9

Pazienza, Giovanni E., Robert Kozma y Robinson E. Pino. Advances in Neuromorphic Memristor Science and Applications. Springer Netherlands, 2016.

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10

Advances In Neuromorphic Memristor Science And Applications. Springer, 2012.

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11

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications. MDPI, 2020. http://dx.doi.org/10.3390/books978-3-03928-577-8.

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12

Mem-Elements for Neuromorphic Circuits with Artificial Intelligence Applications. Elsevier Science & Technology, 2021.

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13

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications. Elsevier, 2021. http://dx.doi.org/10.1016/c2019-0-03157-3.

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14

Pham, Viet-Thanh y Christos Volos. Mem-Elements for Neuromorphic Circuits with Artificial Intelligence Applications. Elsevier Science & Technology Books, 2021.

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15

Chemistry, Royal Society of. New Memory Paradigms : Memristive Phenomena and Neuromorphic Applications: Faraday Discussion 213. Royal Society of Chemistry, The, 2019.

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16

Board on Physics and Astronomy, National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Condensed Matter and Materials Research Committee y Paul Timothy Beaton. Frontiers in Memristive Materials for Neuromorphic Processing Applications: Proceedings of a Workshop. National Academies Press, 2021.

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17

National Academies of Sciences, Engineering, and Medicine. Frontiers in Memristive Materials for Neuromorphic Processing Applications: Proceedings of a Workshop. National Academies Press, 2021.

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18

National Academies of Sciences, Engineering, and Medicine. Frontiers in Memristive Materials for Neuromorphic Processing Applications: Proceedings of a Workshop. National Academies Press, 2021.

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19

Dudek, Piotr. Vision. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0014.

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Resumen
Vision is a sensory modality of primary importance to many animal species. The efficient implementation of visual perception is also one of the main challenges in the design of intelligent robotic systems. This chapter reviews the principles of operation and key features of the early stages of biological vision systems. Following the observation that visual information processing starts in the eye, it reviews several approaches to constructing biomimetic artificial vision systems. It presents devices inspired by the morphology of the insects’ compound eyes, and devices tightly integrating image sensing and processing circuitry. These include silicon integrated circuits mimicking the operation of vertebrate retinas, and bio-inspired systems oriented towards machine vision applications, such as dynamic vision sensors and vision chips with pixel-parallel cellular processor arrays. It elucidates the advantages of the near-sensor processing of the visual information, and potential for future developments of neuromorphic vision sensors.
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20

Neuromorphic learning of continuous-valued mappings from noise-corrupted data: Application to real-time adaptive control. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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21

National Aeronautics and Space Administration (NASA) Staff. Neuromorphic Learning of Continuous-Valued Mappings from Noise-Corrupted Data. Application to Real-Time Adaptive Control. Independently Published, 2018.

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