Selection-for-action emerges in neural networks trained to learn spatial associations between stimuli and actions

Simione Luca; Nolfi Stefano

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Selection-for-action emerges in neural networks trained to learn spatial associations between stimuli and actions

机译：动作选择出现在经过训练以学习刺激和动作之间的空间关联的神经网络中

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《Cognitive processing》 |2015年第suppla期|共1页
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Simione Luca; Nolfi Stefano;
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入库时间 2022-08-18 09:37:10

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1. Selection-for-action emerges in neural networks trained to learn spatial associations between stimuli and actions [J] . Simione Luca, Nolfi Stefano Cognitive processing . 2015,第Suppla期

机译：动作选择出现在经过训练以学习刺激和动作之间的空间关联的神经网络中
2. Pre-Train and Learn:Preserving Global Information for Graph Neural Networks [J] . Dan-Hao Zhu, Xin-Yu Dai, Jia-Jun Chen 计算机科学技术学报（英文版） . 2021,第006期

机译：Pre-Train and Learn:Preserving Global Information for Graph Neural Networks
3. Neural network mosaic model for pupillary responses to spatial stimuli [J] . Sun W., Hung G., Sun FC. Biological Cybernetics: Communication and Control in Organisms and Automata: = Nachrichtenubertragung, Nachrichtenverarbeitung, Steuerung und Regelung in Organismen und in Automaten . 1998,第2期

机译：神经网络镶嵌模型用于瞳孔对空间刺激的反应
4. Stimuli-Responsiveness in Spatially Restricted Polymers: Stimuli-Responsive Transitions and Color-Sensitive Self Repairing Networks [C] . Ying Yang, Marek W. Urban merican Chemical Society. . 2012

机译：空间限制聚合物中的刺激反应性：刺激反应转变和色敏自修复网络
5. Same Data, Same Features: Modern ImageNet-Trained Convolutional Neural Networks Learn the Same Thing [D] . McNeely-White, David G. 2020

机译：相同的数据，特点：现代想象训练卷大神经网络了解同样的事情
6. Modelling spatially-resolved diffuse reflectance spectra of a multi-layered skin model by artificial neural networks trained with Monte Carlo simulations [O] . Sheng-Yang Tsui, Chiao-Yi Wang, Tsan-Hsueh Huang, 2018

机译：通过蒙特卡洛模拟训练的人工神经网络对多层皮肤模型的空间分辨漫反射光谱建模
7. A Neural Network Model with Dopamine-Like Reinforcement Signal That Learns a Spatial Delayed Response Task [O] . R. E. Suri, W. Schultz 1999

机译：具有多巴胺类增强信号的神经网络模型研究空间延迟响应任务

Selection-for-action emerges in neural networks trained to learn spatial associations between stimuli and actions

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