Fig. 4

These represent the temporal filter $v:t\mapsto v(t)$ for different parameters. (a) When $\beta =+\mathrm{\infty }$, we are in the Hebbian linear case of Appendix B.2. The temporal filters are just decaying exponentials which averaged the signal over a past window. (b) When the dynamics of the neurons and synapse are oscillatory damped, some oscillations appear in the temporal filters. The number of oscillations depends on Δ. If Δ is real, then there are no oscillations as in the previous case. However, when Δ becomes a pure imaginary number, it creates a few oscillations which are even more numerous if $|\mathrm{\Delta }|$ increases.