图 书 馆
在尖峰神经网络中使用隐藏单元进行序列学习Sequence learning with hidden units in spiking neural networks |
|
| 课程网址: | http://videolectures.net/nips2011_brea_neural/ |
| 主讲教师: | Johanni Brea |
| 开课单位: | 伯尔尼大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider a statistical framework in which recurrent networks of spiking neurons learn to generate spatio-temporal spike patterns. Given biologically realistic stochastic neuronal dynamics we derive a tractable learning rule for the synaptic weights towards hidden and visible neurons that leads to optimal recall of the training sequences. We show that learning synaptic weights towards hidden neurons significantly improves the storing capacity of the network. Furthermore, we derive an approximate online learning rule and show that our learning rule is consistent with Spike-Timing Dependent Plasticity in that if a presynaptic spike shortly precedes a postynaptic spike, potentiation is induced and otherwise depression is elicited. |
| 关 键 词: | 统计框架; 尖峰神经元; 生物学 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-06：lxf |
| 阅读次数: | 79 |