title: A neural circuit model forming semantic network with exception using spike-timing-dependent plasticity of inhibitory synapses.
abstract: We propose a neural circuit model forming a semantic network with exceptions using the spike-timing-dependent plasticity (STDP) of inhibitory synapses. To evaluate the proposed model, we conducted nine types of computer simulation by combining the three STDP rules for inhibitory synapses and the three spike pairing rules. The simulation results obtained with the STDP rule for inhibitory synapses by Haas et al. [Haas, J.S., Nowotny, T., Abarbanel, H.D.I., 2006, Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. J. Neurophysiol. 96, 3305-3313] are successful, whereas, the other results are unsuccessful. The results and examinations suggested that the inhibitory connection from the concept linked with an exceptional feature to the general feature is necessary for forming a semantic network with an exception.
authors.affiliation: Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka, Tenpaku-cho, Toyohashi 441-8580, Japan. mura@tutkie.tut.ac.jp
authors.forename: Kazushi
authors.initials: K
authors.lastname: Murakoshi
authors.forename: Kyoji
authors.initials: K
authors.lastname: Suganuma
mesh_terms: D000200:Action Potentials / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D008568:Memory / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D012660:Semantics; D013569:Synapses / Q000502:physiology; D049490:Systems Biology