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循环神经网络的完整表现可以支持持续的知觉Over-complete representations on recurrent neural networks can support persistent percepts |
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| 课程网址: | http://videolectures.net/nips2010_druckmann_ocr/ |
| 主讲教师: | Shaul Druckmann |
| 开课单位: | 霍华德休斯医学院 |
| 开课时间: | 2011-01-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A striking aspect of cortical neural networks is the divergence of a relatively small number of input channels from the peripheral sensory apparatus into a large number of cortical neurons, an over-complete representation strategy. Cortical neurons are then connected by a sparse network of lateral synapses. Here we propose that such architecture may increase the persistence of the representation of an incoming stimulus, or a percept. We demonstrate that for a family of networks in which the receptive field of each neuron is re-expressed by its outgoing connections, a represented percept can remain constant despite changing activity. We term this choice of connectivity REceptive FIeld REcombination (REFIRE) networks. The sparse REFIRE network may serve as a high-dimensional integrator and a biologically plausible model of the local cortical circuit. |
| 关 键 词: | 皮质神经网络; 皮质神经元; 局部皮层电路模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-06：zyk |
| 阅读次数: | 57 |