A computational framework for understanding decision making through integration of basic learning rules.
A computational framework for understanding decision making through integration of basic learning rules.
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DOI：
10.1523/jneurosci.4145-12.2013
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发表时间：
2013-03-27
期刊：
影响因子：
--
通讯作者：
Smith BH
中科院分区：
文献类型：
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作者：
Bazhenov M;Huerta R;Smith BH
非联想学习规则和联想学习规则同时修改神经回路。然而，目前还不清楚这些形式的可塑性是如何相互作用来产生条件反应的。在这里，我们将非联想条件反射和联想条件反射整合到蜜蜂嗅觉学习的统一模型中。蜜蜂在经历了一系列的条件反射试验后，反应会相当突然地增加。在接触了非关联试验后，这种突然变化的发生需要进行比仅仅使用关联条件反射更多的试验。我们发现，无监督学习规则和监督学习规则的交互作用对于解释潜在抑制现象是至关重要的。联合条件反射与输出神经元之间的相互抑制相结合，尽管突触权重变化平稳，但仍会产生性能的突然提高。结果表明，使用扇出连接性和神经抑制实现的一套完整的学习规则可以解释关于学习行为的广泛的实验数据。
Nonassociative and associative learning rules simultaneously modify neural circuits. However, it remains unclear how these forms of plasticity interact to produce conditioned responses. Here we integrate nonassociative and associative conditioning within a uniform model of olfactory learning in the honeybee. Honeybees show a fairly abrupt increase in response after a number of conditioning trials. The occurrence of this abrupt change takes many more trials after exposure to nonassociative trials than just using associative conditioning. We found that the interaction of unsupervised and supervised learning rules is critical for explaining latent inhibition phenomenon. Associative conditioning combined with the mutual inhibition between the output neurons produces an abrupt increase in performance despite smooth changes of the synaptic weights. The results show that an integrated set of learning rules implemented using fan-out connectivities together with neural inhibition can explain the broad range of experimental data on learning behaviors.