Building functional networks of spiking model neurons.
Building functional networks of spiking model neurons.
复制标题
DOI：
10.1038/nn.4241
复制
发表时间：
2016-03
影响因子：
25
通讯作者：
Memmesheimer RM
中科院分区：
文献类型：
--
作者：
Abbott LF;DePasquale B;Memmesheimer RM
计算机科学家使用的大多数网络和神经科学建模人员研究的许多网络将单位活动表示为连续变量。然而，神经元主要通过不连续的尖峰信号进行交流。我们回顾了将我们构建有趣的网络的能力从人工连续域转移到更现实的尖峰网络模型的方法。这些方法提出了一些值得进一步理论和实验研究的问题。
Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.
影响因子：
4.3
作者：
Friedrich J;Senn W
通讯作者：
Senn W