Emergence of highly nonrandom functional synaptic connectivity through STDP

Authors:
Hideyuki Kato;Tohru Ikeguchi
Affiliations:
Graduate School of Science and Engineering, Saitama University, Saitama city, Saitama, Japan;Graduate School of Science and Engineering, Saitama University, Saitama city, Saitama, Japan and Saitama University, Brain Science Institute, Saitama city, Saitama, Japan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

Citing 2
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Abstract

We investigated the network topology organized through spike-timingdependent 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.