Department of Bioengineering, Imperial College London, London, UK.
Nat Neurosci. 2019 Jul;22(7):1168-1181. doi: 10.1038/s41593-019-0415-2. Epub 2019 Jun 24.
The hippocampus is able to rapidly learn incoming information, even if that information is only observed once. Furthermore, this information can be replayed in a compressed format in either forward or reverse modes during sharp wave-ripples (SPW-Rs). We leveraged state-of-the-art techniques in training recurrent spiking networks to demonstrate how primarily interneuron networks can achieve the following: (1) generate internal theta sequences to bind externally elicited spikes in the presence of inhibition from the medial septum; (2) compress learned spike sequences in the form of a SPW-R when septal inhibition is removed; (3) generate and refine high-frequency assemblies during SPW-R-mediated compression; and (4) regulate the inter-SPW interval timing between SPW-Rs in ripple clusters. From the fast timescale of neurons to the slow timescale of behaviors, interneuron networks serve as the scaffolding for one-shot learning by replaying, reversing, refining, and regulating spike sequences.
海马体能够快速学习传入的信息,即使这些信息只被观察过一次。此外,在尖波涟漪(SPW-R)期间,该信息可以以正向或反向的压缩格式回放。我们利用训练递归尖峰网络的最先进技术,展示了主要的中间神经元网络如何实现以下功能:(1)生成内部θ序列,以在中隔抑制的情况下结合外部诱发的尖峰;(2)在去除中隔抑制时,以 SPW-R 的形式压缩学习的尖峰序列;(3)在 SPW-R 介导的压缩期间生成和细化高频集合;(4)在涟漪簇中调节 SPW-R 之间的 SPW 间隔定时。从神经元的快速时间尺度到行为的缓慢时间尺度,中间神经元网络作为回放、反转、细化和调节尖峰序列的一次性学习的支架。