Nejad Kevin Kermani, Anastasiades Paul, Hertäg Loreen, Costa Rui Ponte
Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
Bristol Computational Neuroscience Unit, Intelligent Systems Lab, Faculty of Engineering, University of Bristol, Bristol, BS8 1TH, United Kingdom.
Nat Commun. 2025 Jul 4;16(1):6178. doi: 10.1038/s41467-025-61399-5.
The neocortex constructs an internal representation of the world, but the underlying circuitry and computational principles remain unclear. Inspired by self-supervised learning algorithms, we propose a computational theory in which layer 2/3 (L2/3) integrates past sensory input, relayed via layer 4, with top-down context to predict incoming sensory stimuli. Learning is self-supervised by comparing L2/3 predictions with the latent representations of actual sensory input arriving at L5. We demonstrate that our model accurately predicts sensory information in context-dependent temporal tasks, and that its predictions are robust to noisy and occluded sensory input. Additionally, our model generates layer-specific sparsity, consistent with experimental observations. Next, using a sensorimotor task, we show that the model's L2/3 and L5 prediction errors mirror mismatch responses observed in awake, behaving mice. Finally, through manipulations, we offer testable predictions to unveil the computational roles of various cortical features. In summary, our findings suggest that the multi-layered neocortex empowers the brain with self-supervised predictive learning.
新皮层构建了一个关于世界的内部表征，但底层的神经回路和计算原理仍不清楚。受自监督学习算法的启发，我们提出了一种计算理论，其中第2/3层（L2/3）将通过第4层中继的过去感官输入与自上而下的上下文信息整合起来，以预测即将到来的感官刺激。通过将L2/3的预测与到达第5层的实际感官输入的潜在表征进行比较，学习过程实现了自我监督。我们证明，我们的模型能够在依赖上下文的时间任务中准确预测感官信息，并且其预测对噪声和遮挡的感官输入具有鲁棒性。此外，我们的模型产生了特定层的稀疏性，这与实验观察结果一致。接下来，通过一个感觉运动任务，我们表明该模型的L2/3和L5预测误差反映了在清醒、行为活跃的小鼠中观察到的失配反应。最后，通过各种操作，我们提供了可测试的预测，以揭示各种皮层特征的计算作用。总之，我们的研究结果表明，多层新皮层赋予大脑自我监督预测学习的能力。
