Many cognitive and behavioral tasks—such as interval timing, spatial navigation, motor control, and speech—require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal activity can be generated in chaotic and noisy spiking recurrent neural networks. We propose a general solution for networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control, and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain.

中文翻译：

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通过复用神经振荡来学习尖峰网络中的长时间序列

许多认知和行为任务——例如间隔计时、空间导航、运动控制和言语——需要执行精确定时的神经激活序列，而这不能用一系列外部刺激来完全解释。我们展示了如何在混乱和嘈杂的尖峰循环神经网络中生成可重复且可靠的时空活动模式。我们提出了一种通用解决方案，让网络通过提供多周期振荡信号作为输入来自主产生丰富的活动模式。我们证明该模型可以准确地学习各种任务，包括语音生成、运动控制和空间导航。此外，该模型执行自然口语单词的时间重新缩放，并表现出涉及时间处理的实验数据中常见的顺序神经活动。在空间导航的背景下，该模型学习并重放位置细胞的压缩序列，并捕获神经活动的特征，例如波纹的出现和 θ 相进动。总之，我们的研究结果表明，将振荡神经元输入与不同频率相结合提供了一种关键机制，可以在大脑循环回路中生成精确定时的活动序列。