Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear “ramping” component of each neuron’s firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

我们的决定通常取决于由时间延迟分隔的多种感官体验。大脑可以记住这些经历，同时估计事件之间的时间安排。为了了解工作记忆和时间编码的机制，我们分析了四项非人类灵长类动物实验中延迟期间记录的神经活动。为了消除潜在机制的歧义，我们提出了两种分析，即从神经数据中解码时间的流逝并计算神经轨迹随时间的累积维数。在计时信息相关的任务中可以高精度地解码时间，而在与执行任务无关的任务中可以以较低的精度解码时间。神经轨迹总是被观察到是低维的。此外，我们的结果进一步限制了时间编码的机制，因为我们发现每个神经元放电率的线性“斜坡”分量强烈地促成缓慢的时间尺度变化，从而使解码时间成为可能。这些限制排除了依赖于持续、持续的活动和具有高维轨迹的神经网络（如水库网络）的工作记忆模型。相反，通过反向传播训练的循环网络捕获时间编码属性和数据中观察到的维度。