Corticostriatal synaptic weight evolution in a two-alternative forced choice task: a computational stud
Corticostriatal synaptic weight evolution in a two-alternative forced choice task: a computational stud
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两种替代强制选择任务中的皮质纹状体突触权重演化：计算研究
DOI：
10.1016/j.cnsns.2019.105048
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Rubin, J
中科院分区：
文献类型：
--
作者：
Vich, C;Dunovan, K;Verstynen, T;Rubin, J
在自然环境中，哺乳动物可以根据嘈杂的感官信号有效地选择行动，并迅速适应意想不到的结果，以更好地利用未来出现的机会。这种基于反馈的行为变化部分依赖于皮质-基底神经节-丘脑网络内的长期可塑性，其由对纹状体的直接和间接通路神经元的皮质输入的多巴胺能调制驱动。虽然纹状体神经元的放电率已被证明在一系列反馈条件下适应，但仍然难以直接评估促成这些自适应放电率的皮质纹状体突触重量变化。在这项工作中，我们模拟皮质纹状体突触权重的演变的基础上，由多巴胺信号，这是由两个替代的强迫选择任务的背景下的行动的结果驱动的尖峰时间依赖的可塑性规则。我们的研究结果建立1）这个可塑性模型可以成功地学习选择最有价值的行动，2）在有效的制度可塑性主要影响直接途径权重，发展到驱动行动选择向更有回报的行动，和3）可以有共同激活的对立人口内选定的行动渠道，如实验观察。该模型的性能也同意先前在人类受试者中使用概率奖励范例进行的行为实验的结果。
In natural environments, mammals can efficiently select actions based on noisy sensory signals and quickly adapt to unexpected outcomes to better exploit opportunities that arise in the future. Such feedback-based changes in behavior rely, in part, on long term plasticity within cortico-basal-ganglia-thalamic networks, driven by dopaminergic modulation of cortical inputs to the direct and indirect pathway neurons of the striatum. While the firing rates of striatal neurons have been shown to adapt across a range of feedback conditions, it remains difficult to directly assess the corticostriatal synaptic weight changes that contribute to these adaptive firing rates. In this work, we simulate the evolution of corticostriatal synaptic weights based on a spike timing-dependent plasticity rule driven by dopamine signaling that is induced by outcomes of actions in the context of a two-alternative forced choice task. Our results establish 1) that this plasticity model can successfully learn to select the most rewarding actions available, 2) that in the effective regime plasticity predominantly impacts direct pathway weights, evolving to drive action selection toward a more-rewarded action, and 3) that there can be coactivation of opposing populations within selected action channels, as observed experimentally. The model performance also agrees with the results of behavioral experiments carried out previously in human subjects using probabilistic reward paradigms.