Zhong Lin, Baptista Scott, Gattoni Rachel, Arnold Jon, Flickinger Daniel, Stringer Carsen, Pachitariu Marius
HHMI Janelia Research Campus, Ashburn, VA, USA.
Nature. 2025 Jun 18. doi: 10.1038/s41586-025-09180-y.
Representation learning in neural networks may be implemented with supervised or unsupervised algorithms, distinguished by the availability of instruction. In the sensory cortex, perceptual learning drives neural plasticity, but it is not known whether this is due to supervised or unsupervised learning. Here we recorded populations of up to 90,000 neurons simultaneously from the primary visual cortex (V1) and higher visual areas (HVAs) while mice learned multiple tasks, as well as during unrewarded exposure to the same stimuli. Similar to previous studies, we found that neural changes in task mice were correlated with their behavioural learning. However, the neural changes were mostly replicated in mice with unrewarded exposure, suggesting that the changes were in fact due to unsupervised learning. The neural plasticity was highest in the medial HVAs and obeyed visual, rather than spatial, learning rules. In task mice only, we found a ramping reward-prediction signal in anterior HVAs, potentially involved in supervised learning. Our neural results predict that unsupervised learning may accelerate subsequent task learning, a prediction that we validated with behavioural experiments.
神经网络中的表征学习可以通过有监督或无监督算法来实现，这取决于是否有指导信息。在感觉皮层中，知觉学习驱动神经可塑性，但尚不清楚这是由于有监督学习还是无监督学习。在这里，我们在小鼠学习多个任务时，以及在无奖励地暴露于相同刺激期间，同时记录了来自初级视觉皮层（V1）和高级视觉区域（HVA）的多达90,000个神经元群体。与之前的研究类似，我们发现任务小鼠的神经变化与其行为学习相关。然而，这些神经变化在无奖励暴露的小鼠中大多也会出现，这表明这些变化实际上是由于无监督学习。神经可塑性在HVA内侧最高，并遵循视觉而非空间学习规则。仅在任务小鼠中，我们在前部HVA中发现了一个逐渐增强的奖励预测信号，可能参与有监督学习。我们的神经学结果预测，无监督学习可能会加速后续的任务学习，我们通过行为实验验证了这一预测。
