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. ⋯ 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.
Neural Netw. 2015 Feb 1; 62: 11-9.
Abstract 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.Copyright © 2014 Elsevier Ltd. All rights reserved.
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