Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany.
Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany.
PLoS Comput Biol. 2024 Aug 22;20(8):e1012355. doi: 10.1371/journal.pcbi.1012355. eCollection 2024 Aug.
A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of the rodent visual system seems to conflict with this hypothesis. Here, we used an optimized information-theoretic and classical autocorrelation analysis to show that information- and correlation timescales of spiking activity increase along the anatomical hierarchy of the mouse visual system under visual stimulation, while information-theoretic predictability decreases. Moreover, intrinsic timescales for spontaneous activity displayed a similar hierarchy, whereas the hierarchy of predictability was stimulus-dependent. We could reproduce these observations in a basic recurrent network model with correlated sensory input. Our findings suggest that the rodent visual system employs intrinsic mechanisms to achieve longer integration for higher cortical areas, while simultaneously reducing predictability for an efficient neural code.
大脑面临的核心挑战之一是跨各种时间尺度处理信息。这可以通过内在机制（例如，递归耦合或适应）实现时间处理的分层组织来实现，但来自啮齿动物视觉系统尖峰记录的最新证据似乎与这一假设相矛盾。在这里，我们使用优化的信息论和经典自相关分析表明，在视觉刺激下，随着视觉系统解剖层次结构的增加，尖峰活动的信息和相关时间尺度增加，而信息论可预测性降低。此外，自发活动的内在时间尺度表现出相似的层次结构，而可预测性的层次结构则取决于刺激。我们可以在具有相关感觉输入的基本递归网络模型中重现这些观察结果。我们的研究结果表明，啮齿动物视觉系统利用内在机制为更高的皮质区域实现更长的整合，同时为有效的神经代码降低可预测性。
