The brain constantly processes information encoded in temporal sequences of spiking activity. This sequential activity emerges from sensory inputs as well as from the brain's own recurrent connectivity and spans multiple dynamically changing timescales. Decoding the temporal order of spiking activity across these varying timescales is a critical function of the brain, but we do not yet understand its neural implementation. The problem is, that the passive dynamics of neural membrane potentials occur on a short millisecond timescale, whereas many cognitive tasks require the integration of information across much slower behavioral timescales. However, actively generated dendritic plateau potentials do occur on such longer timescales, and their essential role for many aspects of cognition has been firmly established by recent experiments. Here, we build on these discoveries and propose a new model of neural computation that emerges from the interaction of localized plateau potentials across a functionally compartmentalized dendritic tree. We show how this interaction offers a robust solution to the timing invariant detection and processing of sequential spike patterns in single neurons. Stochastic synaptic transmission complements the deterministic all-or-none plateau process and improves information transmission by allowing ensembles of neurons to produce graded responses to continuous combinations of features. We found that networks of such neurons can solve highly complex sequence detection tasks by breaking down long inputs into sequences of shorter, random features that can be classified reliably. These results suggest that active dendritic processes are fundamental to neural computation.

中文翻译：

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树突状平台电位可以处理跨多个时间尺度的尖峰序列

大脑不断处理以脉冲活动的时间序列编码的信息。这种顺序活动来自感觉输入以及大脑自身的循环连接，并跨越多个动态变化的时间尺度。在这些不同的时间尺度上解码尖峰活动的时间顺序是大脑的一个关键功能，但我们还不了解它的神经实现。问题是，神经膜电位的被动动力学发生在短毫秒时间尺度上，而许多认知任务需要跨更慢的行为时间尺度整合信息。然而，在如此长的时间尺度上确实会发生主动产生的树突高原电位，并且最近的实验已经牢固地确立了它们在认知的许多方面的重要作用。这里，我们在这些发现的基础上提出了一种新的神经计算模型，该模型源于局部高原电位在功能划分的树突树上的相互作用。我们展示了这种交互如何为单个神经元中的时序脉冲模式的时序不变检测和处理提供稳健的解决方案。随机突触传递补充了确定性的全或无平台过程，并通过允许神经元集合对特征的连续组合产生分级响应来改善信息传递。我们发现，此类神经元网络可以通过将长输入分解为可以可靠分类的较短随机特征序列来解决高度复杂的序列检测任务。