The contributions of entorhinal cortex and hippocampus to error driven learning.
The contributions of entorhinal cortex and hippocampus to error driven learning.
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DOI：
10.1038/s42003-021-02096-z
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发表时间：
2021-05-24
影响因子：
5.9
通讯作者：
Suzuki WA
中科院分区：
文献类型：
--
作者：
Ku SP;Hargreaves EL;Wirth S;Suzuki WA
计算模型提出内侧颞叶（MTL）对错误驱动的学习有重要贡献，尽管这一假设几乎没有直接的体内证据。为了测试这一点，我们记录了内嗅皮层（EC）和海马（HPC）的猕猴进行联想学习任务，使用错误驱动的学习策略，定义为更好的性能后，错误相对于正确的试验。错误检测信号在EC中相对于HPC更为突出。在学习早期，海马神经元而不是EC神经元通过增加错误试验后的群体刺激选择性来发出错误驱动学习的信号。在另一个没有使用错误驱动学习的任务中，没有看到同样的模式。学习后，EC和HPC中的不同细胞群发出信号，表明新学习的关联具有增强的刺激选择性反应的长期记忆。这些结果表明，EC和HPC从错误中学习的显着，但差异的贡献和EC在错误检测中的特别重要的作用。Ku等人在关联学习任务期间记录猕猴的内嗅皮层（EC）和海马（HPC），以测试计算模型预测它们有助于错误驱动学习。他们表明，EC和HPC有突出的，但差异的贡献，从错误中学习，与EC有一个特别突出的作用，在错误检测。
Computational models proposed that the medial temporal lobe (MTL) contributes importantly to error-driven learning, though little direct in-vivo evidence for this hypothesis exists. To test this, we recorded in the entorhinal cortex (EC) and hippocampus (HPC) as macaques performed an associative learning task using an error-driven learning strategy, defined as better performance after error relative to correct trials. Error-detection signals were more prominent in the EC relative to HPC. Early in learning hippocampal but not EC neurons signaled error-driven learning by increasing their population stimulus-selectivity following error trials. This same pattern was not seen in another task where error-driven learning was not used. After learning, different populations of cells in both the EC and HPC signaled long-term memory of newly learned associations with enhanced stimulus-selective responses. These results suggest prominent but differential contributions of EC and HPC to learning from errors and a particularly important role of the EC in error-detection. Ku et al. recorded in the entorhinal cortex (EC) and hippocampus (HPC) of macaques during associative learning tasks in order to test the computational model prediction that they contribute to error-driven learning. They demonstrate that the EC and HPC have prominent but differential contributions to learning from errors, with the EC having a particularly prominent role in error-detection.
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