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Neuromodulatory Adaptive Combination of Correlation-based Learning in Cerebellum and Reward-based Learning in Basal Ganglia for Goal-directed Behavior Control

机译:小脑相关学习和基底神经节奖励学习的神经调节适应性结合,用于目标定向行为控制。

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摘要

Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.
机译:生物系统中的目标导向决策广泛地基于有条件和无条件刺激之间的关联。这可以进一步分类为经典条件(基于相关的学习)和操作条件(基于奖励的学习)。许多计算和实验研究已经很好地确立了基底神经节在基于奖励的学习中的作用,而小脑在发展特定条件反应中起着重要的作用。尽管被视为独特的学习系统,但最近的动物实验指出了它们在行为学习中的补充作用,并且还表明了这两个大脑结构之间存在实质性的双向交流。基于这种合作学习的概念,在本文中,我们假设基底神经节和小脑学习系统并行工作并相互影响。我们设想这种相互作用受丘脑的奖励调制异突触可塑性(RMHP)规则影响,指导总体目标定向行为。使用基底神经节的递归神经网络参与者评论模型和基于前馈相关性的小脑学习模型,我们证明RMHP规则可以有效地平衡两个学习系统的结果。使用四轮机器人在静态和动态配置下进行觅食任务时,使用日益复杂的模拟环境进行了测试。尽管以简化的生物学抽象水平建模,但我们清楚地证明,与单个系统相比,这种RMHP诱导的组合学习机制可导致对目标导向行为的稳定和快速学习。因此,在本文中,我们通过神经调节可塑性为生物和仿生生物的目标定向决策提供了基底神经节和小脑学习系统自适应组合的计算模型。

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