Figure 5. Learning with an asymmetric spike-timing-dependent plasticity (STDP) rule leads to the absence of backward replay.

(A) Asymmetric STDP kernel used in the learning phase. (B) Learned excitatory recurrent weight matrix. (C) Distribution of nonzero synaptic weights in the weight matrix shown in (B). (D) Pyramidal cell (PC) raster plot on top and PC population rate at the bottom (see Figure 2A) from a simulation run with the weight matrix shown in (B). (E) Posterior matrix of the decoded positions from spikes (see Figure 1E) within a selected high-activity state (sixth one from D). (F) Analysis of selected network dynamics indicators across different E-E weight scaling factors (0.8–1.2) as in Figure 3B.