Figure 2.

Theoretical estimates of stimulation-induced weight dynamics and distributions of time lags for different CR stimulation patterns. Individual columns correspond to CR (A), NCR (B), SCR (C), and SNCR (D) stimulation patterns. Panels show the estimated mean rates of weight change $\langle {\mathcal{J}}_X^{\infty }\rangle$, Equation (4), for intrapopulation (A–D) and interpopulation synapses (A'–D'), respectively. Here, X = “intra,” “inter” marks the considered type of synapses. White curves mark zero contour lines and indicate the boundary between strengthening ($\langle {\mathcal{J}}_X^{\infty }\rangle >0$) and weakening of synapses ($\langle {\mathcal{J}}_X^{\infty }\rangle <0$). Corresponding estimates for the distributions of time lags that lead to weight updates $G_X^{\mathrm{\text{A}}}(t)$ (black), Equation (5), are compared to simulation results (red) in (A”–D”) for intrapopulation synapses and in (A”'–D”') for interpopulation synaspes, respectively. In (A”–D”,A”'–D”'), we set N_s = 2 and f_CR = 10 Hz. Networks were simulated for 90 s of ongoing stimulation. Time lags have been recorded from 400 pairs of pre- and postsynaptic neurons. Pairs were sorted according to synapse types “intra” and “inter” and histograms were calculated using a bin size of 1 ms. Theoretical estimates for ${\mathcal{J}}_X^{\infty }$ were obtained by numerical calculations of $p_{\mathrm{\text{X}}}^{\mathrm{\text{A}}}(s)$, Equation (17). To this end, the time interval [−1, 000, 1, 000] ms was discretized using a binsize of dt = 0.01 ms. Then, G_X(t) was obtained using Equation (5). To compare theoretical estimates and simulation results, we plotted G_X(t)dt in (A”–D”,A”'–D”') and normalized the histograms such that counts summed up to two. Parameters: t_d = 3 ms, η = 0.02, τ₊ = 10 ms, τ_R = 4, β = 1.4.