Continual Learning in a Multi-Layer Network of an Electric Fish
Continual Learning in a Multi-Layer Network of an Electric Fish
复制标题
电鱼多层网络中的持续学习
DOI：
10.1016/j.cell.2019.10.020
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发表时间：
2019
期刊：
影响因子：
64.5
通讯作者：
Sawtell, Nathaniel B.
中科院分区：
文献类型：
--
作者：
Muller, Salomon Z.;Zadina, Abigail N.;Abbott, L.F.;Sawtell, Nathaniel B.
在人工神经网络中，跨多层分布学习已经证明是非常强大的。然而，人们对多层学习如何在大脑中实现知之甚少。在这里，我们提供了一个学习的帐户在多个处理层的电感觉叶（ELL）的鱼，并报告它如何解决众所周知的问题，从机器学习。因为ELL连续地操作和学习，所以它必须在不切换其操作模式的情况下协调学习和信令功能。我们表明，这是通过一个功能区室化的中间层神经元，其中输入驱动学习差异影响树突和轴突尖峰。我们还发现，基于学习而不是感觉反应选择性的连接确保了突触到中间层神经元的可塑性与输出神经元的要求相匹配。我们发现的机制与小脑、海马和大脑皮层以及人工系统的学习有关。
Distributing learning across multiple layers has proven extremely powerful in artificial neural networks. However, little is known about how multi-layer learning is implemented in the brain. Here, we provide an account of learning across multiple processing layers in the electrosensory lobe (ELL) of mormyrid fish and report how it solves problems well known from machine learning. Because the ELL operates and learns continuously, it must reconcile learning and signaling functions without switching its mode of operation. We show that this is accomplished through a functional compartmentalization within intermediate layer neurons in which inputs driving learning differentially affect dendritic and axonal spikes. We also find that connectivity based on learning rather than sensory response selectivity assures that plasticity at synapses onto intermediate-layer neurons is matched to the requirements of output neurons. The mechanisms we uncover have relevance to learning in the cerebellum, hippocampus, and cerebral cortex, as well as in artificial systems.
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DOI：
10.1523/jneurosci.18-15-06009.1998
发表时间：
1998
期刊：
The Journal of Neuroscience
影响因子：
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发表时间：
2016-01-08
期刊：
Science (New York, N.Y.)
影响因子：
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作者：
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影响因子：
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