Marius Pachitariu研究小组报道了生物神经网络的无监督预训练。2025年6月18日，国际知名学术期刊《自然》发表了这一成果。

在这里，该课题组研究人员记录了在小鼠学习多个任务时，以及在无奖励暴露于相同刺激时，同时来自初级视觉皮层（V1）和高级视觉区（HVAs）的多达90,000个神经元的数量。与之前的研究类似，课题组研究人员发现任务小鼠的神经变化与它们的行为学习相关。

然而，这些神经变化在没有奖励的情况下被复制，这表明这些变化实际上是由于无监督的学习。内侧HVAs的神经可塑性最高，服从视觉学习规则，而不是空间学习规则。仅在任务小鼠中，研究组发现HVAs前部的奖励预测信号上升，可能与监督学习有关。他们的神经学研究结果预测，无监督学习可能会加速后续任务的学习，该团队用行为实验验证了这一预测。

研究人员表示，神经网络中的表示学习可以用监督或无监督算法来实现，以指令的可用性来区分。在感觉皮层中，感知学习驱动神经可塑性，但尚不清楚这是由于监督学习还是非监督学习。

附：英文原文

Title: Unsupervised pretraining in biological neural networks

Author: Zhong, Lin, Baptista, Scott, Gattoni, Rachel, Arnold, Jon, Flickinger, Daniel, Stringer, Carsen, Pachitariu, Marius

Issue&Volume: 2025-06-18

Abstract: 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 plasticity1,2,3,4,5,6,7,8,9,10,11,12,13, 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.

DOI: 10.1038/s41586-025-09180-y

Source: https://www.nature.com/articles/s41586-025-09180-y