Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD 20892-4415.
Proc Natl Acad Sci U S A. 2022 May 31;119(22):e2121331119. doi: 10.1073/pnas.2121331119. Epub 2022 May 27.
Adolescent development is characterized by an improvement in multiple cognitive processes. While performance on cognitive operations improves during this period, the ability to learn new skills quickly, for example, a new language, decreases. During this time, there is substantial pruning of excitatory synapses in cortex and specifically in prefrontal cortex. We have trained a series of recurrent neural networks to solve a working memory task and a reinforcement learning (RL) task. Performance on both of these tasks is known to improve during adolescence. After training, we pruned the networks by removing weak synapses. Pruning was done incrementally, and the networks were retrained during pruning. We found that pruned networks trained on the working memory task were more resistant to distraction. The pruned RL networks were able to produce more accurate value estimates and also make optimal choices more consistently. Both results are consistent with developmental improvements on these tasks. Pruned networks, however, learned some, but not all, new problems more slowly. Thus, improvements in task performance can come at the cost of flexibility. Our results show that overproduction and subsequent pruning of synapses is a computationally advantageous approach to building a competent brain.
青少年的发展特点是多种认知过程的改善。在此期间，认知操作的表现有所提高，例如，快速学习新技能的能力，例如，一种新的语言，会下降。在此期间，皮质和特别是前额皮质中的兴奋性突触大量修剪。我们已经训练了一系列递归神经网络来解决工作记忆任务和强化学习 (RL) 任务。已知这两个任务的表现都在青少年时期有所提高。训练后，我们通过去除弱突触来修剪网络。修剪是逐步进行的，并且在修剪过程中对网络进行重新训练。我们发现，经过工作记忆任务训练的修剪网络更能抵抗干扰。修剪后的 RL 网络能够更准确地估计价值，并且更一致地做出最佳选择。这两个结果都与这些任务的发展改善一致。然而，修剪后的网络学习一些但不是所有的新问题都更慢。因此，任务表现的提高可能是以灵活性为代价的。我们的结果表明，突触的过度产生和随后的修剪是构建一个有能力的大脑的计算上有利的方法。
