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Books on the topic 'Neuromorphic applications'

Author: Grafiati
Published: 6 September 2023
Last updated: 26 July 2025

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1

Kozma, Robert, Robinson E. Pino, and Giovanni E. Pazienza, eds. Advances in Neuromorphic Memristor Science and Applications. 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. Springer Netherlands, 2012.

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3

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

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4

C, Merrill Walter, and 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. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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5

Bartolozzi, Chiara, Emre O. Neftci, and 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, and 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, and Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.

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8

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

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9

Pazienza, Giovanni E., Robert Kozma, and 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

Suñé, Jordi. Memristors for Neuromorphic Circuits and Artificial Intelligence Applications. Mdpi AG, 2020.

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

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15

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

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16

Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications. Elsevier Science & Technology, 2024.

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17

Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications. Elsevier Science & Technology Books, 2024.

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18

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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19

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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20

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

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21

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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22

Safa, Ali, and Lars Keuninckx. Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems: Applications in Drone Navigation and Radar Sensing Actions. Springer, 2024.

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23

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

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Abstract:
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 imag
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24

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

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25

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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