Abstract
We consider clustered small-world networks (SWNs) with two inhibitory (I) and excitatory (E) populations. This I-E neuronal network has adaptive dynamic I to E and E to I interpopulation synaptic strengths, governed by interpopulation spike-timing-dependent plasticity (STDP) [i.e., I to E inhibitory STDP (iSTDP) and E to I excitatory STDP (eSTDP)]. In previous works with-out STDPs, fast sparsely synchronized rhythms, related to diverse cognitive functions, were found to appear in a range of noise intensity D for static synaptic strengths. Here, by varying D, we investigate the effect of interpopulation STDPs on diverse population and individual properties of synchronized rhythms that emerge in the I- and the E-populations. Depending on values of D, long-term potentiation (LTP) and long-term depression (LTD) for population-averaged values of saturated interpopulation synaptic strengths are found to occur, and they make effects on the degree of population synchronization. In a broad region of intermediate D, the degree of good synchronization (with higher spiking measure) becomes decreased, while in a region of large D, the degree of bad synchronization (with lower spiking measure) gets increased. Consequently, in each I- or E-population, the synchronization degree becomes nearly the same in a wide range of D (including the intermediate and the large D regions). This kind of “equalization effect” is found to occur via cooperative interplay between the average occupation and pacing degrees of synchronized rhythms. Furthermore, such equalization effect is much more enhanced in the presence of combined I to E and E to I STDPs when compared with the case of individual I to E or E to I STDP. We note that the equalization effect in interpopulation synaptic plasticity is in contrast to the Matthew (bipolarization) effect in intrapopulation (I to I and E to E) synaptic plasticity where good (bad) synchronization gets better (worse). Moreover, emergences of LTP and LTD of interpopulation synaptic strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times.