论文标题
发电的原则
Principles for generation of reverberation
论文作者
论文摘要
在现代神经科学中，已经假定记忆存储在神经回路中，因为顺序尖峰列车和混响是特定的例子之一。FormerResearch在现象描述上取得了很大的进步。但是，混响的机制尚不清楚。 在这项研究中，将电生理记录和数值模拟结合在一起，我们证实了以前未实现的神经元特性，这对于混响中的爆发产生是必需的。 其次，我们发现了连续模式产生的机制，该机制通过网络拓扑和异步神经递质释放清楚地解释。此外，我们还开发了一条可以手动设置顺序设计网络火灾的管道。 第三，我们探讨了STDP学习的动态，并追逐了STDP规则在混响中的影响。有了这些理解，我们制定了一个基于STDP的学习规则，该规则可能会驱动网络以记住任何前提序列。 这些结果表明，神经元电路可以通过STDP规则记住畸形。这些信息存储在Synapse连接中。通过这种方式，动物记得信息是尖峰序列模式。
In modern neuroscience, memory has been postulated to stored in neural circuits as sequential spike train and Reverberation is one of the specific example.Former research has made much progress on phenomenon description. However, the mechanism of reverberation has been unclear yet. In this study, combining electrophysiological record and numerical simulation, we confirmed a formerly unrealized neuron property that is necessary for the burst generation in reverberation. Secondly, we find out the mechanism of sequential pattern generation which clearly explained by network topology and asynchronous neurotransmitter release. In addition, we also developed a pipeline that could design the network fire in manually set order. Thirdly, we explored the dynamics of STDP learning and chased down the effects of STDP Rule in reverberation. With these understandings, we developed a STDP based learning rule which could drive the network to remember any presupposed sequence. These results indicated that neuron circuit can remember malformation through STDP rule. Those information are stored in synapse connections. By this way, animals remember information as spike sequence pattern.
