We investigated the network topology organized through spike-timing-dependent plasticity (STDP) using pair- and triad-connectivity patterns, considering difference of excitatory and inhibitory neurons.
As a result, we found that inhibitory synaptic strength affects statistical properties of the network topology organized through STDP more strongly than the bias of the external input rate for the excitatory neurons. In addition, we also found that STDP leads highly nonrandom structure to the neural network. Our analysis from a viewpoint of connectivity pattern transitions reveals that STDP does not uniformly strengthen and depress excitatory synapses in neural networks. Further, we also found that the significance of triad-connectivity patterns after the learning results from the fact that the probability of triad-connectivity-pattern transitions is much higher than that of combinations of pair-connectivity-pattern transitions.