Wang Quan, Rothkopf Constantin A, Triesch Jochen
Frankfurt Institute for Advanced Studies, Ruth-Moufang Str. 1, 60438 Frankfurt, Germany.
Centre for Cognitive Science & Institute of Psychology, Technical University Darmstadt, Darmstadt, Germany.
PLoS Comput Biol. 2017 Aug 2;13(8):e1005632. doi: 10.1371/journal.pcbi.1005632. eCollection 2017 Aug.
The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.
学习序列行为的能力是我们大脑的一项基本特性。然而，包括最近对成年人类受试者运动序列学习进行研究的一系列长期实验，却产生了许多令人困惑且看似矛盾的结果。特别是，当受试者必须学习多个动作序列时，学习有时会受到前摄干扰和倒摄干扰效应的影响。然而，在其他情况下，学习会加速，如促进和迁移效应所反映的那样。目前尚不清楚导致这些不同发现的潜在神经机制是什么。在这里，我们表明，一个最近开发的循环神经网络模型能够轻易地重现这一系列不同的发现。自组织循环神经网络（SORN）模型是一个由循环连接的阈值单元组成的网络，它将一种简化形式的尖峰时间依赖可塑性（STDP）与确保网络稳定性的稳态可塑性机制相结合，即内在可塑性（IP）和突触归一化（SN）。当在以最近实验为模型的序列学习任务上进行训练时，我们发现它能重现各种干扰、促进和迁移效应。我们展示了这些效应如何根植于网络在学习过程中对不同序列不断变化的内部表征，以及它们如何依赖于训练计划和任务相似性的相互作用。此外，由于模型中的学习基于基本的神经元可塑性机制，该模型揭示了这些可塑性机制最终如何对网络的序列学习能力负责。特别是，我们发现所有这三种可塑性机制对于网络学习不同训练序列的有效内部模型都是必不可少的。形成有效内部模型的这种能力也是观察到的干扰和促进效应的基础。这表明STDP、IP和SN可能是我们学习复杂动作序列能力背后的驱动力。
