Stochastic spike synchronization in a small-world neural network with spike-timing-dependent plasticity

다국어 초록 (Multilingual Abstract)

<P><B>Abstract</B></P> <P>We consider the Watts–Strogatz small-world network (SWN) consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). In previous works without STDP, stochastic spike synchronization (SSS) between noise-induced spikings of subthreshold neurons was found to occur in a range of intermediate noise intensities. Here, we investigate the effect of additive STDP on the SSS by varying the noise intensity. Occurrence of a “Matthew” effect in synaptic plasticity is found due to a positive feedback process. As a result, good synchronization gets better via long-term potentiation of synaptic strengths, while bad synchronization gets worse via long-term depression. Emergences of long-term potentiation and long-term depression of synaptic strengths are intensively investigated via microscopic studies based on the pair-correlations between the pre- and the post-synaptic IISRs (instantaneous individual spike rates) as well as the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, the effects of multiplicative STDP (which depends on states) on the SSS are studied and discussed in comparison with the case of additive STDP (independent of states). These effects of STDP on the SSS in the SWN are also compared with those in the regular lattice and the random graph.</P> <P><B>Highlights</B></P> <P> <UL> <LI> An excitatory small-world network of subthreshold neurons is considered. </LI> <LI> Effect of spike-timing-dependent plasticity on spike synchronization is investigated. </LI> <LI> Both the additive and the multiplicative case are considered. </LI> <LI> A “Matthew” effect in synaptic plasticity is found to occur. </LI> <LI> Emergence of potentiation and depression is investigated via microscopic studies. </LI> </UL> </P>