Delay activity dynamics: task dependent time encoding and low dimensional trajectories
Delay activity dynamics: task dependent time encoding and low dimensional trajectories
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延迟活动动态：任务相关的时间编码和低维轨迹
DOI：
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发表时间：
2018
期刊：
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通讯作者：
Stefano Fusi
中科院分区：
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作者：
Christopher J. Cueva;Encarni Marcos;A. Saez;A. Genovesio;M. Jazayeri;M. Jazayeri;R. Romo;C. Salzman;M. Shadlen;Stefano Fusi
我们的决定往往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（工作记忆），同时，它可以很容易地估计事件之间的时间，这在预测刺激和规划未来行动中起着重要作用。为了更好地理解工作记忆和时间编码的神经机制，我们分析了在非人类灵长类动物的四个不同实验中延迟期间记录的神经活动，并考虑了三类神经网络模型来解释数据：吸引子神经网络，混沌水库网络和通过时间反向传播训练的递归神经网络。为了消除这些模型的歧义，我们提出了两种分析方法：1）从神经数据中解码时间的流逝，2）计算神经轨迹随时间演变的累积维数。我们的分析表明，时间可以解码高精度的任务，定时信息是相关的和较低的精度在任务中，它是不相关的执行任务，这表明工作记忆不需要依赖于固定的活动模式周围的恒定速率。此外，我们的研究结果进一步限制了时间编码的机制，因为我们表明所有数据集的轨迹维数都很低。与此相一致，我们发现每个神经元的放电率的线性“斜坡”分量强烈有助于使解码时间成为可能的缓慢时间尺度变化。我们表明，这些低维斜坡轨迹是有益的，因为它们允许在一个时间点学习的计算在整个时间上推广。我们的观察结果限制了解释数据的可能模型，排除了简单的吸引子模型和随机连接的递归网络（混沌水库网络），这些网络在相对较快的时间尺度上变化，但与通过时间反向传播训练的递归神经网络模型一致。我们的研究结果表明，一个强大的新工具，研究的相互作用的时间加工和工作记忆的客观分类的电生理活动。
Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences (working memory) and, at the same time, it can easily estimate the timing between events, which plays a fundamental role in anticipating stimuli and planning future actions. To better understand the neural mechanisms underlying working memory and time encoding we analyze neural activity recorded during delays in four different experiments on non-human primates and we consider three classes of neural network models to explain the data: attractor neural networks, chaotic reservoir networks and recurrent neural networks trained with backpropagation through time. To disambiguate these models we propose two analyses: 1) decoding the passage of time from neural data, and 2) computing the cumulative dimensionality of the neural trajectory as it evolves over time. Our analyses reveal that time can be decoded with high precision in tasks where timing information is relevant and with lower precision in tasks where it is irrelevant to perform the task, suggesting that working memory need not rely on constant rates around a fixed activity pattern. In addition, our results further constrain the mechanisms underlying time encoding as we show that the dimensionality of the trajectories is low for all datasets. Consistent with this, 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. We show that these low dimensional ramping trajectories are beneficial as they allow computations learned at one point in time to generalize across time. Our observations constrain the possible models that explain the data, ruling out simple attractor models and randomly connected recurrent networks (chaotic reservoir networks) that vary on relatively fast timescales, but agree with recurrent neural network models trained with backpropagation through time. Our results demonstrate a powerful new tool for studying the interplay of temporal processing and working memory by objective classification of electrophysiological activity.