Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Yulia Sandamirskaya"'
Autor:
Raphaela Kreiser, Alpha Renner, Vanessa R. C. Leite, Baris Serhan, Chiara Bartolozzi, Arren Glover, Yulia Sandamirskaya
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal network is fully realized in Intel's neuromorphic research chip, Loihi, and precisely integrates
Externí odkaz:
https://doaj.org/article/c1b5875607224a7581377681c2dceac4
Publikováno v:
Frontiers in Neurorobotics, Vol 13 (2019)
Neurally inspired robotics already has a long history that includes reactive systems emulating reflexes, neural oscillators to generate movement patterns, and neural networks as trainable filters for high-dimensional sensory information. Neural inspi
Externí odkaz:
https://doaj.org/article/74677a91483b4859a8fa5143a699583d
Publikováno v:
Frontiers in Neuroscience, Vol 12 (2018)
Neuromorphic Very Large Scale Integration (VLSI) devices emulate the activation dynamics of biological neuronal networks using either mixed-signal analog/digital or purely digital electronic circuits. Using analog circuits in silicon to physically em
Externí odkaz:
https://doaj.org/article/034478c895034a449b4d7853c62e7a84
Publikováno v:
Frontiers in Computational Neuroscience, Vol 11 (2017)
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, homogeneous, and recurrently connected neural networks based on a mean field approach. Within dynamic field theory, the DNFs have been used as building
Externí odkaz:
https://doaj.org/article/82916c573765428997bdc17776d71e84
Autor:
Moritz B. Milde, Hermann Blum, Alexander Dietmüller, Dora Sumislawska, Jörg Conradt, Giacomo Indiveri, Yulia Sandamirskaya
Publikováno v:
Frontiers in Neurorobotics, Vol 11 (2017)
Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement
Externí odkaz:
https://doaj.org/article/45c8a8aa44274e9394f65edcf5e10de8
Autor:
Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Olshausen, Bruno A., Yulia Sandamirskaya, Sommer, Friedrich T., Paxon Frady, E.
Publikováno v:
Web of Science
Inferring the position of objects and their rigid transformations is still an open problem in visual scene understanding. Here we propose a neuromorphic solution that utilizes an efficient factorization network based on three key concepts: (1) a comp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c63d6152e32a3b5178177eb1bb5d9376
http://arxiv.org/abs/2208.12880
http://arxiv.org/abs/2208.12880
Publikováno v:
Proceedings of the International Conference on Neuromorphic Systems 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c42e93cbb7d3ab5233021e8d486d10d4
https://doi.org/10.1145/3546790.3546820
https://doi.org/10.1145/3546790.3546820
Publikováno v:
Proceedings of the International Conference on Neuromorphic Systems 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31364a08b1007caf29f8cbde539409ea
https://doi.org/10.1145/3546790.3546804
https://doi.org/10.1145/3546790.3546804
Publikováno v:
Proceedings of the International Conference on Neuromorphic Systems 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3513468dc13f9b6068fa8223d16f70ae
https://doi.org/10.1145/3546790.3546791
https://doi.org/10.1145/3546790.3546791
Autor:
Yulia Sandamirskaya
Publikováno v:
Science robotics. 7(67)
Emerging computing hardware systems address the need of robotic AI for robust, fast, and efficient computation for a variety of tasks.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73f1d9455f03d6ce1525532e3724b72a
https://pubmed.ncbi.nlm.nih.gov/35767648
https://pubmed.ncbi.nlm.nih.gov/35767648