Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences, Grisebachstraße 5, 37077, Göttingen, Germany.
Perception and Plasticity Group, German Primate Center - Leibniz Institute for Primate Research, Kellnerweg 4, 37077, Göttingen, Germany.
Nat Commun. 2024 Aug 21;15(1):7196. doi: 10.1038/s41467-024-51543-y.
Distinguishing faces requires well distinguishable neural activity patterns. Contextual information may separate neural representations, leading to enhanced identity recognition. Here, we use functional magnetic resonance imaging to investigate how predictions derived from contextual information affect the separability of neural activity patterns in the macaque face-processing system, a 3-level processing hierarchy in ventral visual cortex. We find that in the presence of predictions, early stages of this hierarchy exhibit well separable and high-dimensional neural geometries resembling those at the top of the hierarchy. This is accompanied by a systematic shift of tuning properties from higher to lower areas, endowing lower areas with higher-order, invariant representations instead of their feedforward tuning properties. Thus, top-down signals dynamically transform neural representations of faces into separable and high-dimensional neural geometries. Our results provide evidence how predictive context transforms flexible representational spaces to optimally use the computational resources provided by cortical processing hierarchies for better and faster distinction of facial identities.
区分面部需要具有良好可区分性的神经活动模式。上下文信息可以分离神经表示，从而提高身份识别能力。在这里，我们使用功能磁共振成像来研究从上下文信息中得出的预测如何影响猕猴面部处理系统中神经活动模式的可分离性，该系统是腹侧视觉皮层中的一个 3 级处理层次结构。我们发现，在存在预测的情况下，该层次结构的早期阶段表现出良好的可分离性和高维神经几何形状，类似于层次结构顶部的神经几何形状。这伴随着调谐特性从较高区域到较低区域的系统转移，赋予较低区域更高阶、不变的表示，而不是它们的前馈调谐特性。因此，自上而下的信号将面部的神经表示动态地转换为可分离的高维神经几何形状。我们的结果提供了证据，证明了预测性上下文如何将灵活的表示空间转换为可分离的高维神经几何形状，以优化利用皮层处理层次结构提供的计算资源，从而更好、更快地区分面部身份。
