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外侧膝状体的时间整合动力学。

Dynamics of Temporal Integration in the Lateral Geniculate Nucleus.

机构信息

Center for Neuroscience, University of California, Davis, Davis, CA 95616.

Center for Vision Science, University of California, Davis, Davis, CA.

出版信息

eNeuro. 2022 Aug 23;9(4). doi: 10.1523/ENEURO.0088-22.2022. Print 2022 Jul-Aug.

Abstract

Before visual information from the retina reaches primary visual cortex (V1), it is dynamically filtered by the lateral geniculate nucleus (LGN) of the thalamus, the first location within the visual hierarchy at which nonretinal structures can significantly influence visual processing. To explore the form and dynamics of geniculate filtering we used data from monosynpatically connected pairs of retinal ganglion cells (RGCs) and LGN relay cells in the cat that, under anesthetized conditions, were stimulated with binary white noise and/or drifting sine-wave gratings to train models of increasing complexity to predict which RGC spikes were relayed to cortex, what we call "relay status." In addition, we analyze and compare a smaller dataset recorded in the awake state to assess how anesthesia might influence our results. Consistent with previous work, we find that the preceding retinal interspike interval (ISI) is the primary determinate of relay status with only modest contributions from longer patterns of retinal spikes. Including the prior activity of the LGN cell further improved model predictions, primarily by indicating epochs of geniculate burst activity in recordings made under anesthesia, and by allowing the model to capture gain control-like behavior within the awake LGN. Using the same modeling framework, we further demonstrate that the form of geniculate filtering changes according to the level of activity within the early visual circuit under certain stimulus conditions. This finding suggests a candidate mechanism by which a stimulus specific form of gain control may operate within the LGN.

摘要

在视网膜的视觉信息到达初级视皮层 (V1) 之前,它会被丘脑的外侧膝状体核 (LGN) 动态过滤,这是视觉层次结构中第一个可以显著影响视觉处理的非视网膜结构的位置。为了探索神经节过滤的形式和动态,我们使用了来自猫的单突触连接的视网膜神经节细胞 (RGC) 和 LGN 中继细胞的数据,在麻醉条件下,用二进制白噪声和/或漂移正弦波光栅刺激来训练越来越复杂的模型,以预测哪些 RGC 尖峰被中继到皮层,我们称之为“中继状态”。此外,我们分析并比较了在清醒状态下记录的较小数据集,以评估麻醉如何影响我们的结果。与之前的工作一致,我们发现,前一个视网膜尖峰间隔 (ISI) 是中继状态的主要决定因素,只有视网膜尖峰的稍长模式才有适度的贡献。包括 LGN 细胞的先前活动进一步提高了模型预测,主要是通过在麻醉记录中指示神经节爆发活动的时期,并允许模型在清醒的 LGN 中捕获增益控制样行为。使用相同的建模框架,我们进一步证明,根据特定刺激条件下早期视觉电路内的活动水平,神经节过滤的形式会发生变化。这一发现为在 LGN 中可能存在的刺激特定形式的增益控制提供了一种候选机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1551/9402337/9df41adac4c4/ENEURO.0088-22.2022_f001.jpg

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