Center for Neural Science, New York University, New York, NY, USA.
DeepMind, London, UK.
Nat Commun. 2021 Oct 13;12(1):5982. doi: 10.1038/s41467-021-25939-z.
Many sensory-driven behaviors rely on predictions about future states of the environment. Visual input typically evolves along complex temporal trajectories that are difficult to extrapolate. We test the hypothesis that spatial processing mechanisms in the early visual system facilitate prediction by constructing neural representations that follow straighter temporal trajectories. We recorded V1 population activity in anesthetized macaques while presenting static frames taken from brief video clips, and developed a procedure to measure the curvature of the associated neural population trajectory. We found that V1 populations straighten naturally occurring image sequences, but entangle artificial sequences that contain unnatural temporal transformations. We show that these effects arise in part from computational mechanisms that underlie the stimulus selectivity of V1 cells. Together, our findings reveal that the early visual system uses a set of specialized computations to build representations that can support prediction in the natural environment.
许多感官驱动的行为依赖于对环境未来状态的预测。视觉输入通常沿着复杂的时间轨迹演变，很难外推。我们通过构建遵循更直接时间轨迹的神经表示来测试早期视觉系统中的空间处理机制是否有助于预测的假设。我们在麻醉猕猴中记录了 V1 群体的活动，同时呈现了从简短视频剪辑中提取的静态帧，并开发了一种测量相关神经群体轨迹曲率的程序。我们发现 V1 群体自然地使图像序列变直，但纠缠于包含不自然时间变换的人工序列。我们表明，这些影响部分源于 V1 细胞的刺激选择性所基于的计算机制。总之，我们的发现揭示了早期视觉系统使用一组专门的计算来构建能够支持自然环境中预测的表示。
