Logarithmic distributions prove that intrinsic learning is Hebbian.
Logarithmic distributions prove that intrinsic learning is Hebbian.
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DOI：
10.12688/f1000research.12130.1
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发表时间：
2017-01-01
期刊：
影响因子：
--
通讯作者：
Scheler, Gabriele
中科院分区：
文献类型：
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作者：
Scheler, Gabriele
在本文中，我们记录对数正态分布的尖峰率，突触的重量和内在兴奋性（增益）的神经元在不同的大脑区域，如听觉或视觉皮层，海马，小脑，纹状体，中脑核。我们发现了一个显着的一致性的重尾，特别是对数正态分布率，重量和收益在所有的大脑区域。强递归和前馈连接（皮质与纹状体和小脑），神经递质（GABA（纹状体）或谷氨酸（皮质））或激活水平（皮质低，浦肯野细胞和中脑核高）之间的差异与此特征无关。权重和收益的对数标度分布似乎是一种到处存在的函数性质。其次，我们创建了一个通用的神经模型来证明Hebbian学习将创建和维护对数正态分布。我们可以用模型证明，不仅权重，而且内在增益，都需要有强大的赫布学习，以产生和维护实验证明的分布。这解决了一个长期存在的问题，即内在兴奋性所表现出的可塑性类型。
In this paper, we document lognormal distributions for spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears as a functional property that is present everywhere. Secondly, we created a generic neural model to show that Hebbian learning will create and maintain lognormal distributions. We could prove with the model that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This settles a long-standing question about the type of plasticity exhibited by intrinsic excitability.