Emergence and maintenance of modularity in neural networks with Hebbian and anti-Hebbian inhibitory STDP
Fig 2
Consolidation of imperfectly learned memories.
(A) Temporal evolution of the connectivity matrix during spontaneous activity in the absence of stimulation. Initial connectivity with two unfinished modules at t = 0 is reinforced over time. The excitatory (inhibitory) connections are marked as in Fig 1. (B) Evolution of the distribution of link weights (probability density functions, PDFs, in a linear-logarithmic scale) in the connectivity matrices at t = 0s (light green), t = 400s (cyan) and t = 4000s (magenta). (C-J) Evolution of the network activity and various metrics in absence of stimulation for a sample of 30 seconds. Some spontaneous recalls are highlighted, for population P1 (green shade) and P2 (orange shade). Pink shadow marks an epoch of asynchronous irregular firing activity without recalls. (C) Raster plot with excitatory (inhibitory) neurons marked in red (blue). (D) PDFs of the coefficient of variations for all the neurons during the entire simulation (grey) and for a homogeneous Poisson process (yellow). (E) Instantaneous Kuramoto order parameters R for the networks (gray) and for neurons in population P1 (green) and P2 (orange), and their corresponding PDFs over the entire simulation (F). (G) Temporal evolution of the mean firing rates for populations P1 and P2, and (H) their corresponding frequency distributions (in linear-logarithmic scale) showing a peak at 2 Hz and long time tail. (I) Instantaneous change rates of synaptic weights in both populations P1 and P2 over time and their PDF (in linear-logarithmic scale) (J) over the entire simulation time, showing the prevalence of positive weight changes (reinforcement) with respect to negative ones (depression).