Methods for reducing interference in the Complementary Learning Systems model: Oscillating inhibition and autonomous memory rehearsal
Methods for reducing interference in the Complementary Learning Systems model: Oscillating inhibition and autonomous memory rehearsal
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DOI：
10.1016/j.neunet.2005.08.010
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发表时间：
2005-11-01
期刊：
影响因子：
7.8
通讯作者：
Perotte, AJ
中科院分区：
文献类型：
--
作者：
Norman, KA;Newman, EL;Perotte, AJ
稳定性-可塑性问题（即大脑如何将新信息融入其世界模型，同时保留现有知识）几十年来一直处于计算记忆研究的前沿。在本文中，我们批判性地评估了海马-皮质相互作用的补充学习系统理论如何很好地解决稳定性-可塑性问题。我们确定了该模型的两个主要挑战：找到一种针对皮层和海马体的学习算法，该算法可以选择性地强化弱记忆，并选择性地惩罚竞争记忆；并防止在非静态环境下发生灾难性遗忘（即，当项目暂时从训练集中删除时）。然后，我们讨论这些问题的潜在解决方案：首先，我们描述了一种最近开发的学习算法，该算法利用神经振荡来找到记忆的薄弱部分（因此它们可以得到加强）和强大的竞争者（因此它们可以受到惩罚），并且我们展示了该算法如何优于其他学习算法（CPCA Hebbian 学习和 Leabra 在记忆重叠模式方面）。其次，我们描述了在 REM 睡眠期间如何自主重新激活记忆（分别在皮层和海马体中），并结合振荡学习算法可以降低环境中不再存在的输入模式的遗忘率。然后，我们简单演示了该过程如何防止 AB-AC 学习范式中的灾难性干扰 (c) 2005 Elsevier Ltd. 保留所有权利。
The stability-plasticity problem (i.e. how the brain incorporates new information into its model of the world, while at the same time preserving existing knowledge) has been at the forefront of computational memory research for several decades. In this paper, we critically evaluate how well the Complementary Learning Systems theory of hippocampo-cortical interactions addresses the stability-plasticity problem. We identify two major challenges for the model: Finding a learning algorithm for cortex and hippocampus that enacts selective strengthening of weak memories, and selective punishment of competing memories; and preventing catastrophic forgetting in the case of non-stationary environments (i.e. when items are temporarily removed from the training set). We then discuss potential solutions to these problems: First, we describe a recently developed learning algorithm that leverages neural oscillations to find weak parts of memories (so they can be strengthened) and strong competitors (so they can be punished), and we show how this algorithm outperforms other learning algorithms (CPCA Hebbian learning and Leabra at memorizing overlapping patterns. Second, we describe how autonomous re-activation of memories (separately in cortex and hippocampus) during REM sleep, coupled with the oscillating learning algorithm, can reduce the rate of forgetting of input patterns that are no longer present in the environment. We then present a simple demonstration of how this process can prevent catastrophic interference in an AB-AC learning paradigm. (c) 2005 Elsevier Ltd. All rights reserved.