Bursting dynamics remarkably improve the performance of neural networks on liquid computing
Bursting dynamics remarkably improve the performance of neural networks on liquid computing
复制标题
爆发动力学显着提高了神经网络在液体计算上的性能
DOI：
10.1007/s11571-016-9387-z
复制
发表时间：
2016-04
影响因子：
3.7
通讯作者：
Xue Fangzheng
中科院分区：
文献类型：
--
作者：
Li Xiumin;Chen Qing;Xue Fangzheng
Burst firings are functionally important behaviors displayed by neural circuits, which plays a primary role in reliable transmission of electrical signals for neuronal communication. However, with respect to the computational capability of neural networks, most of relevant studies are based on the spiking dynamics of individual neurons, while burst firing is seldom considered. In this paper, we carry out a comprehensive study to compare the performance of spiking and bursting dynamics on the capability of liquid computing, which is an effective approach for intelligent computation of neural networks. The results show that neural networks with bursting dynamic have much better computational performance than those with spiking dynamics, especially for complex computational tasks. Further analysis demonstrate that the fast firing pattern of bursting dynamics can obviously enhance the efficiency of synaptic integration from pre-neurons both temporally and spatially. This indicates that bursting dynamic can significantly enhance the complexity of network activity, implying its high efficiency in information processing.
登录
查看更多内容
噪声神经网络中的放电同步和时间顺序
影响因子：
3.7
作者：
Shi Xia;Wang Qingyun;Lu Qishao
通讯作者：
Lu Qishao
DOI：
10.1016/s0165-1684(03)00039-2
发表时间：
2003-06
期刊：
Signal Process.
影响因子：
--
作者：
Aditya A. Saha;G. V. Anand
通讯作者：
Aditya A. Saha;G. V. Anand
影响因子：
--
作者：
Izhikevich, EM
通讯作者：
Izhikevich, EM
影响因子：
3.7
作者：
H. Jaeger
通讯作者：
H. Jaeger
DOI：
--
发表时间：
2005
期刊：
--
影响因子：
--
作者：
H. Burgsteiner
通讯作者：
H. Burgsteiner