• DocumentCode
    1928090
  • Title

    Different inhibitory effects by dopaminergic modulation and global suppression of activity

  • Author

    Hayashi, Takuji ; Araki, Osamu ; Ikeguchi, Tohm

  • Author_Institution
    Dept. of Appl. Phys., Tokyo Univ. of Sci., Japan
  • Volume
    4
  • fYear
    2003
  • fDate
    20-24 July 2003
  • Firstpage
    2563
  • Abstract
    In the dopaminergic network system of prefrontal cortex (PFC)-ventral tegmental area (VTA), physiological experiments have been reported that the D2 neurons inhibit the spontaneous activity of PFC neurons. However, the functional role of D2 suppression is not understood well. Is the effect of modulatory D2 inhibition different from that of GABAergic inhibition? The aim of this research is to reveal the difference between modulatory suppression of D2 and global inhibition by interneurons. To compare the effects, we construct two alternative models: (1) all GABAergic interneurons of PFC are modulated by a D2 system, or (2) a global interneuron depolarizes; all of PFC pyramidal cells. In computer simulations, we exemplify each of the models using a spiking neural network model with sparse and random synaptic connections. The simulation result shows that model-(1) keeps high correlation between spatial patterns of mean firing rates and the network structure despite the suppression of activity, while model-(2) reduces the correlation. This result suggests that modulatory suppression of D2 is more than a global suppression and may play a role in memory retrieval function.
  • Keywords
    learning (artificial intelligence); neural nets; D2 neurons; D2 suppression; dopaminergic modulation; dopaminergic network system; global suppression; inhibitory effects; memory retrieval function; prefrontal cortex ventral tegmental area; random synaptic connections; spiking neural network model; Brain modeling; Chemicals; Computational modeling; Computer networks; Computer simulation; Lead; Learning; Neural networks; Neurons; Physics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1223969
  • Filename
    1223969