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pro vyhledávání: '"Cleland Thomas A"'
Dennler et al. submit that they have discovered limitations affecting some of the conclusions drawn in our 2020 paper, Rapid online learning and robust recall in a neuromorphic olfactory circuit. Specifically, they assert (1) that the public dataset
Externí odkaz:
http://arxiv.org/abs/2411.10456
The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains are adept at learning stable representations given small samples of noisy observations; across sensor
Externí odkaz:
http://arxiv.org/abs/2409.18396
Autor:
Yang, Chen, Cleland, Thomas A.
Annolid is a deep learning-based software package designed for the segmentation, labeling, and tracking of research targets within video files, focusing primarily on animal behavior analysis. Based on state-of-the-art instance segmentation methods, A
Externí odkaz:
http://arxiv.org/abs/2403.18690
Animal behavior analysis plays a crucial role in various fields, such as life science and biomedical research. However, the scarcity of available data and the high cost associated with obtaining a large number of labeled datasets pose significant cha
Externí odkaz:
http://arxiv.org/abs/2312.07723
Autor:
Cook, Jack A., Cleland, Thomas A.
We present a generalized theoretical framework for olfactory representation and plasticity, using the theory of smooth manifolds and sheaves to depict categorical odor learning via distributed neural computation. Beginning with the space of all possi
Externí odkaz:
http://arxiv.org/abs/2208.07455
Publikováno v:
The Oxford Companion to the Book, 1 ed., 2010.
Externí odkaz:
http://www.oxfordreference.com/view/10.1093/acref/9780198606536.001.0001/acref-9780198606536-e-1054
Autor:
Christensen, Dennis V., Dittmann, Regina, Linares-Barranco, Bernabé, Sebastian, Abu, Gallo, Manuel Le, Redaelli, Andrea, Slesazeck, Stefan, Mikolajick, Thomas, Spiga, Sabina, Menzel, Stephan, Valov, Ilia, Milano, Gianluca, Ricciardi, Carlo, Liang, Shi-Jun, Miao, Feng, Lanza, Mario, Quill, Tyler J., Keene, Scott T., Salleo, Alberto, Grollier, Julie, Marković, Danijela, Mizrahi, Alice, Yao, Peng, Yang, J. Joshua, Indiveri, Giacomo, Strachan, John Paul, Datta, Suman, Vianello, Elisa, Valentian, Alexandre, Feldmann, Johannes, Li, Xuan, Pernice, Wolfram H. P., Bhaskaran, Harish, Furber, Steve, Neftci, Emre, Scherr, Franz, Maass, Wolfgang, Ramaswamy, Srikanth, Tapson, Jonathan, Panda, Priyadarshini, Kim, Youngeun, Tanaka, Gouhei, Thorpe, Simon, Bartolozzi, Chiara, Cleland, Thomas A., Posch, Christoph, Liu, Shih-Chii, Panuccio, Gabriella, Mahmud, Mufti, Mazumder, Arnab Neelim, Hosseini, Morteza, Mohsenin, Tinoosh, Donati, Elisa, Tolu, Silvia, Galeazzi, Roberto, Christensen, Martin Ejsing, Holm, Sune, Ielmini, Daniele, Pryds, N.
Publikováno v:
Neuromorph. Comput. Eng. 2 022501 (2022)
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This dat
Externí odkaz:
http://arxiv.org/abs/2105.05956
Autor:
Carroll, James T.
Publikováno v:
New York History, 2001 Jan 01. 82(1), 96-97.
Externí odkaz:
https://www.jstor.org/stable/42677762
Autor:
Borthakur, Ayon, Cleland, Thomas A.
The mammalian olfactory system learns rapidly from very few examples, presented in unpredictable online sequences, and then recognizes these learned odors under conditions of substantial interference without exhibiting catastrophic forgetting. We hav
Externí odkaz:
http://arxiv.org/abs/1907.05827
Autor:
Imam, Nabil, Cleland, Thomas A.
Publikováno v:
Nature Machine Intelligence 2 (2020): 181-191
We present a neural algorithm for the rapid online learning and identification of odorant samples under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system. As with biological olfact
Externí odkaz:
http://arxiv.org/abs/1906.07067