Bursting dynamics and network structural changes towards and away from a Pavlovian-conditioned neural network
Fig 1
Schematic illustration of an Izhikevich model network undergoing Pavlovian learning.
The network consists of 1,600 excitatory neurons (filled red dots) and 400 inhibitory neurons (blue circles), interconnected with a sparsity of 0.1. The thickness of each connecting arrow represents the synaptic weight strength. To facilitate the Pavlovian conditioning protocol, 100 subpopulations are formed, each comprising 100 randomly selected neurons from the overall population (only 4 subpopulations, each of which contains only 3 neurons, are shown in the scaled-down schematic diagram). During the conditioning process, a non-rewarded electrical stimulation is applied to a randomly chosen subpopulation at a time interval between 100 to 300 ms. In contrast, a rewarded stimulation is administered to a specific subpopulation (S1) at approximately 5-second intervals. The synaptic weights evolve dynamically in accordance with the three-factor dopamine-modulated STDP model described in the Model Section.