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Titlebook: Brain-Inspired Information Technology; Akitoshi Hanazawa,Tsutom Miki,Keiichi Horio Book 2010 Springer-Verlag Berlin Heidelberg 2010 Inform

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31#
發(fā)表于 2025-3-26 23:39:13 | 只看該作者
George W. Gokel,Stephen H. Korzeniowskiopriate values of parameters simultaneously improved the performance of the predictor of the environment and increase the external rewards compared with the conventional reinforcement learning. In conclusions, the relation between the proposed method and active learning, diversive curiosity, and specific curiosity is also discussed.
32#
發(fā)表于 2025-3-27 04:38:55 | 只看該作者
The Origins of Macroeconometric Modelst is designed as a fully autonomous, un-tethered robot, which fits to the pipes within a diameter range of 200- 300 millimeters. The practical field tests show that the robot performs well and can do pipe inspection properly and the robot moves gracefully not only at straight paths but also at junctions and curves.
33#
發(fā)表于 2025-3-27 08:54:06 | 只看該作者
Akitoshi Hanazawa,Tsutom Miki,Keiichi HorioRecent results in brain inspired information technology
34#
發(fā)表于 2025-3-27 09:56:47 | 只看該作者
35#
發(fā)表于 2025-3-27 17:03:12 | 只看該作者
https://doi.org/10.1007/978-3-642-04025-2Information Technology (IT); Navigation; Processing; behavior; information processing; learning; neural ne
36#
發(fā)表于 2025-3-27 18:51:50 | 只看該作者
978-3-642-26513-6Springer-Verlag Berlin Heidelberg 2010
37#
發(fā)表于 2025-3-27 22:22:22 | 只看該作者
38#
發(fā)表于 2025-3-28 03:10:07 | 只看該作者
39#
發(fā)表于 2025-3-28 06:45:07 | 只看該作者
Die Sammlung — Nutzbarmachung und Funktiones at the level of neuronal cells and ensembles are still not well understood. The chapter explores emulation of multimodal information processing in a brain-like manner through evolving spiking neural network (ESNN) architectures that use several multimodal characteristics of the biological brains,
40#
發(fā)表于 2025-3-28 12:34:06 | 只看該作者
Andreas Ostrowicki,Erich Koepp,Fritz V?gtleavigation system inspired by the structure of the entorhino-hippocampal loop circuitry is described. The local navigation system consists of a landmark extraction unit, a learning-type matching unit and a local route memory unit. In the local route memory unit, the sequence-learning mechanism of the
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