Much of the information the brain processes and stores is temporal in nature—a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds—we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli.

中文翻译：

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将感觉和运动模式编码为循环神经网络中的时不变轨迹

大脑处理和存储的大部分信息本质上都是时间性的——例如，口语或手写签名是由它如何及时展开来定义的。然而，目前尚不清楚神经回路如何编码复杂的时变模式。我们表明，通过调整循环神经网络 (RNN) 的权重，它可以识别并转录口语数字。该模型阐明了皮层网络中的神经动力学如何解决三个基本挑战：首先，将多个随时间变化的感觉和运动模式编码为稳定的神经轨迹；其次，概括相关的空间特征；第三，识别以不同速度播放的相同刺激——我们表明这种时间不变性的出现是因为循环动力学产生了具有适当调制角速度的神经轨迹。