Ritvo Victoria J H, Nguyen Alex, Turk-Browne Nicholas B, Norman Kenneth A
Department of Psychology, Princeton University.
Princeton Neuroscience Institute, Princeton University.
bioRxiv. 2024 Jun 25:2023.04.02.535239. doi: 10.1101/2023.04.02.535239.
What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, these models have recently been challenged by studies showing that pairing two stimuli with a shared associate can sometimes cause differentiation, depending on the parameters of the study and the brain region being examined. Here, we provide a purely unsupervised neural network model that can explain these and other related findings. The model can exhibit integration or differentiation depending on the amount of activity allowed to spread to competitors - inactive memories are not modified, connections to moderately active competitors are weakened (leading to differentiation), and connections to highly active competitors are strengthened (leading to integration). The model also makes several novel predictions - most importantly, that when differentiation occurs as a result of this unsupervised learning mechanism, it will be rapid and asymmetric, and it will give rise to anticorrelated representations in the region of the brain that is the source of the differentiation. Overall, these modeling results provide a computational explanation for a diverse set of seemingly contradictory empirical findings in the memory literature, as well as new insights into the dynamics at play during learning.
是什么决定了记忆的神经表征何时会合并（整合）或分离（分化）？经典的监督学习模型认为，当两个刺激预测相似的结果时，它们的表征应该整合。然而，最近这些模型受到了一些研究的挑战，这些研究表明，将两个具有共同关联物的刺激配对，有时会导致分化，这取决于研究的参数和所检查的脑区。在这里，我们提供了一个纯粹无监督的神经网络模型，它可以解释这些以及其他相关发现。该模型可以根据允许传播到竞争记忆的活动量表现出整合或分化——不活跃的记忆不会被修改，与适度活跃的竞争记忆的连接会被削弱（导致分化），与高度活跃的竞争记忆的连接会被加强（导致整合）。该模型还做出了几个新的预测——最重要的是，当由于这种无监督学习机制而发生分化时，它将是快速且不对称的，并且会在作为分化源的脑区产生反相关的表征。总体而言，这些建模结果为记忆文献中一组看似矛盾的实证发现提供了一种计算解释，同时也为学习过程中起作用的动态机制提供了新的见解。
