Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
Elife. 2023 Oct 16;12:e52599. doi: 10.7554/eLife.52599.
Visual neurons respond selectively to features that become increasingly complex from the eyes to the cortex. Retinal neurons prefer flashing spots of light, primary visual cortical (V1) neurons prefer moving bars, and those in higher cortical areas favor complex features like moving textures. Previously, we showed that V1 simple cell tuning can be accounted for by a basic model implementing temporal prediction - representing features that predict future sensory input from past input (Singer et al., 2018). Here, we show that hierarchical application of temporal prediction can capture how tuning properties change across at least two levels of the visual system. This suggests that the brain does not efficiently represent all incoming information; instead, it selectively represents sensory inputs that help in predicting the future. When applied hierarchically, temporal prediction extracts time-varying features that depend on increasingly high-level statistics of the sensory input.
视觉神经元对从眼睛到大脑皮层的特征的选择性反应越来越复杂。视网膜神经元更喜欢闪烁的光点，初级视觉皮层(V1)神经元更喜欢移动的光条，而那些在更高的皮层区域则更喜欢像移动纹理这样的复杂特征。以前，我们表明 V1 简单细胞的调谐可以通过一个基本模型来解释，该模型实现了时间预测——表示从过去输入中预测未来感觉输入的特征(Singer 等人，2018)。在这里，我们表明，时间预测的分层应用可以捕获调谐特性如何在至少两个视觉系统级别上发生变化。这表明大脑并没有有效地表示所有输入的信息；相反，它选择性地表示有助于预测未来的感觉输入。当分层应用时，时间预测提取了随时间变化的特征，这些特征取决于感觉输入的越来越高级别的统计信息。
