Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks
Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks
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DOI：
10.1038/s41598-018-22077-3
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发表时间：
2018-02-28
影响因子：
4.6
通讯作者：
Rotter, Stefan
中科院分区：
文献类型：
--
作者：
Gallinaro, Julia V.;Rotter, Stefan
基于相关性的赫布可塑性被认为在发育和学习过程中塑造神经元的连接，而稳态可塑性将稳定网络活动。在这里，我们调查另一个，新的方面，这种二分法：赫布的关联属性也出现作为一个网络效应的可塑性规则的基础上，在神经元水平上的稳态原则？为了解决这个问题，我们模拟了一个经常性的网络泄漏集成和消防神经元，其中兴奋性连接受到结构可塑性规则的基础上放电率稳态。我们发现，一个亚组的神经元发展更强的组内连接的结果，接受更强的外部刺激。在一个实验记录良好的情况下，我们表明，功能特定的连接，类似于在啮齿动物的视觉皮层中观察到的，可以出现这样的可塑性规则。由刺激引发的依赖于经验的结构变化是持久的，只有当神经元再次暴露于非特异性外部输入时才会缓慢衰减。
Correlation-based Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another, new aspect of this dichotomy: Can Hebbian associative properties also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level? To address this question, we simulated a recurrent network of leaky integrate-and-fire neurons, in which excitatory connections are subject to a structural plasticity rule based on firing rate homeostasis. We show that a subgroup of neurons develop stronger within-group connectivity as a consequence of receiving stronger external stimulation. In an experimentally well-documented scenario we show that feature specific connectivity, similar to what has been observed in rodent visual cortex, can emerge from such a plasticity rule. The experience-dependent structural changes triggered by stimulation are long-lasting and decay only slowly when the neurons are exposed again to unspecific external inputs.