A spiking neural network based on the basal ganglia functional anatomy
A spiking neural network based on the basal ganglia functional anatomy
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DOI：
10.1016/j.neunet.2015.03.002
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发表时间：
2015-07-01
期刊：
影响因子：
7.8
通讯作者：
Hamker, Fred H.
中科院分区：
文献类型：
--
作者：
Baladron, Javier;Hamker, Fred H.
我们引入了一个能够学习刺激动作关联的基底神经节拼接神经网络。我们在三个主要的基底神经节通路，直接，间接和超直接，尖峰时间依赖性学习和考虑多巴胺的量（奖励）的学习模型。此外，我们允许学习绕过基底神经节的皮质-丘脑通路。因此，该系统为不同的基底神经节通路开发了新的功能：直接通路通过解除对丘脑的抑制来选择行动，超直接通路抑制替代品，间接通路学会抑制常见错误。数值实验表明，该系统是能够学习集的确定性或随机规则。(C)2015爱思唯尔有限公司版权所有。
We introduce a splicing neural network of the basal ganglia capable of learning stimulus action associations. We model learning in the three major basal ganglia pathways, direct, indirect and hyperdirect, by spike time dependent learning and considering the amount of dopamine available (reward). Moreover, we allow to learn a cortico-thalamic pathway that bypasses the basal ganglia. As a result the system develops new functionalities for the different basal ganglia pathways: The direct pathway selects actions by disinhibiting the thalamus, the hyperdirect one suppresses alternatives and the indirect pathway learns to inhibit common mistakes. Numerical experiments show that the system is capable of learning sets of either deterministic or stochastic rules. (C) 2015 Elsevier Ltd. All rights reserved.