Continuous attractors have been used to understand recent neuroscience experiments where persistent activity patterns encode internal representations of external attributes like head direction or spatial location. However, the conditions under which the emergent bump of neural activity in such networks can be manipulated by space and time-dependent external sensory or motor signals are not understood. Here, we find fundamental limits on how rapidly internal representations encoded along continuous attractors can be updated by an external signal. We apply these results to place cell networks to derive a velocity-dependent nonequilibrium memory capacity in neural networks.

中文翻译：

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连续吸引子的非平衡统计力学

连续吸引子已被用于理解最近的神经科学实验，其中持续活动模式编码外部属性的内部表示，如头部方向或空间位置。然而，尚不清楚在何种条件下，此类网络中神经活动的突增可以被空间和时间相关的外部感觉或运动信号操纵。在这里，我们发现了外部信号更新沿连续吸引子编码的内部表示的速度的基本限制。我们将这些结果应用于放置细胞网络，以在神经网络中推导出速度相关的非平衡记忆容量。