Faculty of Education, Kagawa University, 1-1 Saiwai-cho, Takamatsu, Kagawa 760-8521, Japan.
Neural Netw. 2015 Feb;62:11-9. doi: 10.1016/j.neunet.2014.05.024. Epub 2014 Jun 5.
We investigated the organization of a recurrent network under ongoing synaptic plasticity using a model of neural oscillators coupled by dynamic synapses. In this model, the coupling weights changed dynamically, depending on the timing between the oscillators. We determined the phase coupling function of the oscillator model, Γ(ϕ), using conductance-based neuron models. Furthermore, we examined the effects of the Fourier zero mode of Γ(ϕ), which has a critical role in the case of spike-time-dependent plasticity-organized recurrent networks. Heterogeneous layered clusters with different frequencies emerged from homogeneous populations as the Fourier zero mode increased. Our findings may provide new insights into the self-assembly mechanisms of neural networks related to synaptic plasticity.
我们使用由动态突触耦合的神经振荡器模型研究了在持续的突触可塑性下的复发性网络的组织。在该模型中，耦合权重根据振荡器之间的时间关系动态变化。我们使用基于电导的神经元模型确定了振荡器模型的相位耦合函数Γ(ϕ)。此外，我们研究了 Γ(ϕ)的傅里叶零模式的影响，在基于尖峰时间的可塑性组织的复发性网络的情况下，该模式起着关键作用。随着傅里叶零模式的增加，具有不同频率的异质分层簇从均匀群体中出现。我们的研究结果可能为与突触可塑性相关的神经网络的自组装机制提供新的见解。
