Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Baram Sosis, Jonathan E Rubin
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Abstract

Various mathematical models have been formulated to describe the changes in synaptic strengths resulting from spike-timing-dependent plasticity (STDP). A subset of these models include a third factor, dopamine, which interacts with spike timing to contribute to plasticity at specific synapses, notably those from cortex to striatum at the input layer of the basal ganglia. Theoretical work to analyze these plasticity models has largely focused on abstract issues, such as the conditions under which they may promote synchronization and the weight distributions induced by inputs with simple correlation structures, rather than on scenarios associated with specific tasks, and has generally not considered dopamine-dependent forms of STDP. In this paper we introduce forms of dopamine-modulated STDP adapted from previously proposed plasticity rules. We then analyze, mathematically and with simulations, their performance in two biologically relevant scenarios. We test the ability of each of the three models to complete simple value estimation and action selection tasks, studying the learned weight distributions and corresponding task performance in each setting. Interestingly, we find that each plasticity rule is well suited to a subset of the scenarios studied but falls short in others. Different tasks may therefore require different forms of synaptic plasticity, yielding the prediction that the precise form of the STDP mechanism present may vary across regions of the striatum, and other brain areas impacted by dopamine, that are involved in distinct computational functions.

不同的多巴胺能峰值时间依赖的可塑性规则适用于不同的功能角色。
各种数学模型已经制定,以描述突触强度的变化导致的峰值时间依赖的可塑性(STDP)。这些模型的一个子集包括第三个因素,多巴胺,它与脉冲时间相互作用,有助于特定突触的可塑性,特别是从基底神经节输入层的皮层到纹状体的突触。分析这些可塑性模型的理论工作主要集中在抽象问题上,例如它们可能促进同步的条件和简单相关结构输入引起的权重分布,而不是与特定任务相关的场景,并且通常没有考虑多巴胺依赖的STDP形式。在本文中,我们介绍了多巴胺调节的STDP形式,改编自先前提出的可塑性规则。然后,我们用数学方法和模拟方法分析了它们在两种生物学相关情况下的表现。我们测试了三种模型完成简单的值估计和动作选择任务的能力,研究了每种设置下学习到的权重分布和相应的任务性能。有趣的是,我们发现每个可塑性规则都非常适合研究场景的一个子集,但在其他场景中却不适用。因此,不同的任务可能需要不同形式的突触可塑性,由此预测,STDP机制的精确形式可能在纹状体和其他受多巴胺影响的大脑区域之间有所不同,这些区域涉及不同的计算功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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