Rhythmic Inhibition Allows Neural Networks to Search for Maximally Consistent States
Rhythmic Inhibition Allows Neural Networks to Search for Maximally Consistent States
复制标题
节律抑制使神经网络能够搜索最大一致的状态
DOI：
10.1162/neco_a_00785
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发表时间：
2015
影响因子：
2.9
通讯作者：
G. Indiveri
中科院分区：
文献类型：
--
作者：
H. Mostafa;Lorenz K. Muller;G. Indiveri
伽马频段节律性抑制是神经回路中普遍存在的现象，然而其计算作用仍然难以捉摸。我们表明，一个伽马频段节律性抑制模型允许耦合的皮质回路基序网络搜索能最好地使外部输入与网络连接中编码的内部一致性模型相协调的网络配置。我们表明，赫布可塑性使网络能够通过实例学习一致性模型。由节律性抑制驱动的搜索动态使所述网络能够解决困难的约束满足问题，而无需对网络中随机波动的形式做出假设。我们表明，搜索动态可由一个随机采样过程很好地近似。我们使用所述网络重现感知多稳态现象，其转换时间与实验数据非常匹配，并表明它们提供了一个通用的神经框架，可用于对其他感知推理现象进行建模。
Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits, yet its computational role remains elusive. We show that a model of gamma-band rhythmic inhibition allows networks of coupled cortical circuit motifs to search for network configurations that best reconcile external inputs with an internal consistency model encoded in the network connectivity. We show that Hebbian plasticity allows the networks to learn the consistency model by example. The search dynamics driven by rhythmic inhibition enable the described networks to solve difficult constraint satisfaction problems without making assumptions about the form of stochastic fluctuations in the network. We show that the search dynamics are well approximated by a stochastic sampling process. We use the described networks to reproduce perceptual multistability phenomena with switching times that are a good match to experimental data and show that they provide a general neural framework that can be used to model other perceptual inference phenomena.
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影响因子：
13.9
作者：
Buzsáki G;Wang XJ
通讯作者：
Wang XJ
影响因子：
16.2
作者：
Ray, Supratim;Maunsell, John H. R.
通讯作者：
Maunsell, John H. R.
影响因子：
16.2
作者：
Bosman CA;Schoffelen JM;Brunet N;Oostenveld R;Bastos AM;Womelsdorf T;Rubehn B;Stieglitz T;De Weerd P;Fries P
通讯作者：
Fries P
影响因子：
2.5
作者：
Brunel, N;Wang, XJ
通讯作者：
Wang, XJ