Learning with reinforcement prediction errors in a model of the Drosophila mushroom body.
Learning with reinforcement prediction errors in a model of the Drosophila mushroom body.
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DOI：
10.1038/s41467-021-22592-4
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发表时间：
2021-05-07
影响因子：
16.6
通讯作者：
Nowotny T
中科院分区：
文献类型：
--
作者：
Bennett JEM;Philippides A;Nowotny T
在不断变化的环境中进行有效的决策，需要对决策结果进行准确的预测。在果蝇中，这种学习部分是由蘑菇体协调的，其中多巴胺神经元发出加强刺激信号，以调节突触前到蘑菇体输出神经元的可塑性。基于以前的蘑菇体模型，其中多巴胺神经元信号绝对强化，我们建议多巴胺神经元信号强化预测错误，利用反馈强化预测输出神经元。我们制定了可塑性规则，最大限度地减少预测误差，验证输出神经元在模拟中学习准确的强化预测，并假设连接，解释更多的生理观察比实验约束模型。约束和增强模型再现了广泛的条件反射和阻塞实验，我们证明，没有阻塞并不意味着没有预测误差相关的学习。我们的研究结果提供了五个预测，可以使用既定的实验方法进行测试。蘑菇体中的多巴胺神经元帮助果蝇学会接近奖励和避免惩罚。在这里，作者提出了一个模型，其中多巴胺能学习信号通过利用蘑菇体输出神经元的反馈强化预测来编码强化预测误差。
Effective decision making in a changing environment demands that accurate predictions are learned about decision outcomes. In Drosophila, such learning is orchestrated in part by the mushroom body, where dopamine neurons signal reinforcing stimuli to modulate plasticity presynaptic to mushroom body output neurons. Building on previous mushroom body models, in which dopamine neurons signal absolute reinforcement, we propose instead that dopamine neurons signal reinforcement prediction errors by utilising feedback reinforcement predictions from output neurons. We formulate plasticity rules that minimise prediction errors, verify that output neurons learn accurate reinforcement predictions in simulations, and postulate connectivity that explains more physiological observations than an experimentally constrained model. The constrained and augmented models reproduce a broad range of conditioning and blocking experiments, and we demonstrate that the absence of blocking does not imply the absence of prediction error dependent learning. Our results provide five predictions that can be tested using established experimental methods. Dopamine neurons in the mushroom body help Drosophila learn to approach rewards and avoid punishments. Here, the authors propose a model in which dopaminergic learning signals encode reinforcement prediction errors by utilising feedback reinforcement predictions from mushroom body output neurons.
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影响因子：
3.5
作者：
Frémaux N;Gerstner W
通讯作者：
Gerstner W
影响因子：
7.7
作者：
Aso Y;Hattori D;Yu Y;Johnston RM;Iyer NA;Ngo TT;Dionne H;Abbott LF;Axel R;Tanimoto H;Rubin GM
通讯作者：
Rubin GM
影响因子：
5.4
作者：
BUSH, RR;MOSTELLER, F
通讯作者：
MOSTELLER, F
DOI：
10.1523/jneurosci.4145-12.2013
发表时间：
2013-03-27
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Bazhenov M;Huerta R;Smith BH
通讯作者：
Smith BH
影响因子：
4.3
作者：
Cope AJ;Vasilaki E;Minors D;Sabo C;Marshall JAR;Barron AB
通讯作者：
Barron AB