Abstract：
Converging experimental and computational evidence indicate that on the scale of seconds the brain encodes time through changing patterns of neural activity. Experimentally, two general forms of neural dynamic regimes that can encode time have been observed: neural population clocks and ramping activity. Neural population clocks provide a high-dimensional code to generate complex spatiotemporal output patterns, in which each neuron exhibits a nonlinear temporal profile. A prototypical example of neural population clocks are neural sequences, which have been observed across species, brain areas, and behavioral paradigms. Additionally, neural sequences emerge in artificial neural networks trained to solve time-dependent tasks. Here, we examine the role of neural sequences in the encoding of time, and how they may emerge in a biologically plausible manner. We conclude that neural sequences may represent a canonical computational regime to perform temporal computations.
摘要：
融合的实验和计算证据表明，在秒的尺度上，大脑通过改变神经活动的模式来编码时间。实验上,已经观察到两种可以编码时间的神经动力学机制的一般形式:神经群体时钟和斜坡活动。神经群体时钟提供了一个高维代码来生成复杂的时空输出模式，其中每个神经元表现出非线性时间轮廓。神经群体时钟的典型例子是神经序列，在不同物种中观察到的，大脑区域,和行为范式。此外,神经序列出现在训练用来解决时间相关任务的人工神经网络中。这里,我们研究神经序列在时间编码中的作用，以及它们如何以生物学上合理的方式出现。我们得出的结论是，神经序列可能代表执行时间计算的规范计算机制。
