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在模拟神经元发育过程中,大型脉冲神经网络中出现偏好放电序列。

Emergence of preferred firing sequences in large spiking neural networks during simulated neuronal development.

作者信息

Iglesias Javier, Villa Alessandro E P

机构信息

Grenoble Institut des Neurosciences-Equipe 7, Neuro-Heuristic Research Group, Université Joseph Fourier-Site Santé, Domaine de la Merci, La Tronche cedex, France.

出版信息

Int J Neural Syst. 2008 Aug;18(4):267-77. doi: 10.1142/S0129065708001580.

Abstract

Two main processes concurrently refine the nervous system over the course of development: cell death and selective synaptic pruning. We simulated large spiking neural networks (100 x 100 neurons "at birth") characterized by an early developmental phase with cell death due to excessive firing rate, followed by the onset of spike timing dependent synaptic plasticity (STDP), driven by spatiotemporal patterns of stimulation. The cell death affected the inhibitory units more than the excitatory units during the early developmental phase. The network activity showed the appearance of recurrent spatiotemporal firing patterns along the STDP phase, thus suggesting the emergence of cell assemblies from the initially randomly connected networks. Some of these patterns were detected throughout the simulation despite the activity-driven network modifications while others disappeared.

摘要

在神经系统发育过程中,有两个主要过程同时对其进行优化:细胞死亡和选择性突触修剪。我们模拟了大型脉冲神经网络(“出生时”有100×100个神经元),其特征在于早期发育阶段因放电率过高导致细胞死亡,随后在时空刺激模式驱动下,出现了依赖于脉冲时间的突触可塑性(STDP)。在早期发育阶段,细胞死亡对抑制性单元的影响大于兴奋性单元。沿着STDP阶段,网络活动呈现出反复出现的时空放电模式,这表明从最初随机连接的网络中出现了细胞集合。尽管网络因活动而发生改变,但在整个模拟过程中仍检测到了一些这样的模式,而其他一些模式则消失了。

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