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樓主
發(fā)表于 2025-3-21 17:29:24 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Growing Adaptive Machines
編輯Taras Kowaliw,Nicolas Bredeche,René Doursat
視頻videohttp://file.papertrans.cn/390/389039/389039.mp4
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: ;
出版日期Book 2014
版次1
doihttps://doi.org/10.1007/978-3-642-55337-0
isbn_softcover978-3-662-50944-9
isbn_ebook978-3-642-55337-0Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
The information of publication is updating

書目名稱Growing Adaptive Machines影響因子(影響力)




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沙發(fā)
發(fā)表于 2025-3-21 21:44:24 | 只看該作者
Evolving Culture Versus Local Minima,tion operator, and this gives rise to rapid search in the space of communicable ideas that help humans build up better high-level internal representations of their world. These hypotheses put together imply that human culture and the evolution of ideas have been crucial to counter an optimization di
板凳
發(fā)表于 2025-3-22 02:06:21 | 只看該作者
地板
發(fā)表于 2025-3-22 05:21:58 | 只看該作者
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發(fā)表于 2025-3-22 16:50:09 | 只看該作者
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發(fā)表于 2025-3-22 18:35:16 | 只看該作者
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發(fā)表于 2025-3-22 23:58:18 | 只看該作者
Methoden der Stoffwechselphysiologie,t are modified by Hebbian learning and homeostatic plasticity, driven by input patterns from other neural regions and ultimately from the external world. Through an unsupervised developmental process, the model neurons begin?to display the major known functional properties of V1 neurons, including r
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發(fā)表于 2025-3-23 01:49:53 | 只看該作者
https://doi.org/10.1007/978-3-662-42375-2, the other carries out development at an entire network level and evolves rules that change the network (holocentric). In the process, we hope to reveal some important issues and questions that are relevant to researchers wishing to create other such models.
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發(fā)表于 2025-3-23 07:02:05 | 只看該作者
https://doi.org/10.1007/978-3-662-02050-0cial bodies with asymmetrical shapes and patterning; (iii) directed movement of unicellular animats in 2D; and (iv) processing signals at the level of single cells. We also report a recent introduction of spiking neuron models in GReaNs. We then present a road map towards using this system for evolution and development of neural networks.
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