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Oscillations can reconcile slowly changing stimuli with short neuronal integration and STDP timescales

机译:振荡可以通过短暂的神经元整合和STDP时标来调节缓慢变化的刺激

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Oscillatory brain activity has been widely reported experimentally, yet its functional roles, if any, are still under debate. In this review we argue two things: firstly, thanks to oscillations, even slowly changing stimuli can be encoded in precise relative spike times, decodable by downstream "coincidence detector" neurons in a feedforward manner. Secondly, the required connectivity to do so can spontaneously emerge with spike timing-dependent plasticity (STDP), in an unsupervised manner. The key here is that a common oscillatory drive enables neurons to remain under a fluctuation-driven regime. In this regime spike time jitter does not accumulate and can thus be lower than the intrinsic timescales of stimulus fluctuations, which leads to so-called "temporal encoding". Furthermore, the oscillatory drive formats the spikes in discrete oversampling volleys, and the relative spike times between neurons indicate the eventual differences in their activation levels. The oversampling accelerates the STDP-based learning for downstream neurons. After learning, readout only takes one oscillatory cycle. Finally, we also discuss experimental evidence, and the question of how the theory is complementary to the so-called "communication through coherence" theory.
机译:实验性报道了震荡性脑活动,但其功能性作用(如果有的话)仍在争论中。在这篇综述中,我们争论了两点:首先,由于振荡,即使是缓慢变化的刺激也可以以精确的相对尖峰时间编码,下游的“巧合检测器”神经元可以以前馈的方式对其进行解码。其次,这样做所需的连接性会以无监督的方式自发地出现与尖峰时序相关的可塑性(STDP)。这里的关键是常见的振荡驱动使神经元能够保持在波动驱动的状态下。在这种情况下,尖峰时间抖动不会累积,因此可以低于刺激波动的内在时间尺度,从而导致所谓的“时间编码”。此外,振荡驱动器格式化离散的过采样凌空中的尖峰,并且神经元之间的相对尖峰时间表明了它们激活水平的最终差异。过度采样可加速基于STDP的下游神经元学习。学习后,读出仅需要一个振荡周期。最后,我们还讨论了实验证据,以及该理论如何与所谓的“通过连贯交流”理论互补的问题。

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