Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
Neural Plast. 2021 Jan 19;2021:6668175. doi: 10.1155/2021/6668175. eCollection 2021.
Gamma oscillation in neural circuits is believed to associate with effective learning in the brain, while the underlying mechanism is unclear. This paper aims to study how spike-timing-dependent plasticity (STDP), a typical mechanism of learning, with its interaction with gamma oscillation in neural circuits, shapes the network dynamics properties and the network structure formation. We study an excitatory-inhibitory (E-I) integrate-and-fire neuronal network with triplet STDP, heterosynaptic plasticity, and a transmitter-induced plasticity. Our results show that the performance of plasticity is diverse in different synchronization levels. We find that gamma oscillation is beneficial to synaptic potentiation among stimulated neurons by forming a special network structure where the sum of excitatory input synaptic strength is correlated with the sum of inhibitory input synaptic strength. The circuit can maintain E-I balanced input on average, whereas the balance is temporal broken during the learning-induced oscillations. Our study reveals a potential mechanism about the benefits of gamma oscillation on learning in biological neural circuits.
神经回路中的伽马振荡被认为与大脑中的有效学习有关,但其潜在机制尚不清楚。本文旨在研究尖峰时间依赖可塑性(STDP)作为一种典型的学习机制,以及它与神经回路中的伽马振荡相互作用,如何塑造网络动力学特性和网络结构形成。我们研究了一个具有三联体 STDP、异突触可塑性和递质诱导可塑性的兴奋性-抑制性(E-I)整合-触发神经元网络。我们的结果表明,在不同的同步水平下,可塑性的性能是多样化的。我们发现,伽马振荡通过形成一种特殊的网络结构,有利于刺激神经元之间的突触增强,其中兴奋性输入突触强度的总和与抑制性输入突触强度的总和相关。该电路可以平均保持 E-I 平衡输入,而在学习诱导的振荡过程中,平衡会暂时打破。我们的研究揭示了生物神经回路中伽马振荡对学习的有益作用的潜在机制。