节律抑制允许神经网络搜索最大一致状态
Rhythmic inhibition allows neural networks to search for maximally consistent states
摘要：
γ波段节律抑制是神经回路中普遍存在的现象，但其计算作用仍然是难以捉摸的。我们发现，一个模型的伽玛波段的节奏抑制允许网络的耦合皮层电路图案搜索网络配置，最好的协调外部输入与内部一致性模型编码的网络连接。我们表明，赫布可塑性允许网络学习的一致性模型的例子。由节奏抑制驱动的搜索动力学使所描述的网络能够解决困难的约束满足问题，而无需对网络中的随机波动的形式进行假设。我们发现，搜索动态很好地近似随机抽样过程。我们使用所描述的网络再现感知多稳定性现象的切换时间是一个很好的匹配实验数据，并表明它们提供了一个通用的神经框架，可用于模拟其他“感知推理”现象。
英文摘要：
Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits yet its computational role still 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 multi-stability phenomena with switching times that are a good match to experimental data and show that they provide a general neural framework which can be used to model other 'perceptual inference' phenomena.
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