Bursting dynamics and network structural changes towards and away from a Pavlovian-conditioned neural network
Fig 3
Evolution to feed-forward network architecture during Pavlovian conditioning.
(A) Synaptic weight distribution (top) and recurrent loop-length statistics (bottom) at various conditioning stages [t = 0 (magenta), t = 24 s (red), 240 s (green), 3,600 s (blue)]. Loop-length ratios are relative to the networks that are randomly shuffled (black dashed line). (B) Scatter plots illustrating the sum of afferent weights (∑Win) versus the sum of efferent weights (∑Wout) of all excitatory neurons. Orange points identify neurons within the subpopulation (S1)receiving rewarded stimuli, while purple points represent the top 100 neurons with the highest ∑Win values at t = 3,600 s. (C) Two-dimensional “spring-force layout” visualization of the network corresponding to the scatter plot in B (t = 3,600 s). Notably, the central cluster of orange points becomes a “source,’’ and the peripheral group of purple points on the outskirts becomes a “sink.’’ The orange and purple insets highlight intra-connectivity among excitatory neurons in the source (occupying the core area) and in the sink (occupying the peripheral area), respectively: The intra-connections are emphasized with thicker lines (black: weight = 4, white: weight = 0)compared to the dense, fuzzy background inter-connections.