• DocumentCode
    185343
  • Title

    Study of the long-term effect of STDP in areas of spiking neurons

  • Author

    Hulea, M.

  • Author_Institution
    Fac. of Autom. Control & Comput. Eng., Gheorghe Asachi Tech. Univ. of Iasi, Iasi, Romania
  • fYear
    2014
  • fDate
    17-19 Oct. 2014
  • Firstpage
    482
  • Lastpage
    487
  • Abstract
    The spike-timing-dependent-plasticity (STDP) is a mechanism for adjusting the efficacies of biological synapses that was observed and studied in vitro. However, the STDP effect for natural neurons in vivo is subject of debate because in several experiments when the neurons are stimulated indirectly by natural paths the STDP effect was insignificant. Starting from these aspects this work studies by simulation the STDP long-term effect on synaptic plasticity in order to determine whether the long-term potentiation (LTP) and the long-term depression (LTD) could compensate each other during long-term activity of the neural network. The results show that for some synapses the weights start to oscillate in small intervals around long term stable values that are different from the limits of the weights variation interval. This behavior is caused by the fact that, indeed, the effects of LTP and LTD compensate each other at certain weight values when the same pattern of the input stimuli is presented repeatedly to the network input. The LTP and LTD compensation that determines long term weights stability to other values than the weight variation limits could improve the sensitivity of the learning process in the biological networks because no neuron specific limitation is introduced.
  • Keywords
    bioelectric potentials; neurophysiology; STDP long-term effect; biological networks; biological synapses; learning process; long-term activity; long-term depression; long-term potentiation; long-term weights stability; natural neurons; neural network; neuron specific limitation; spike-timing-dependent-plasticity; spiking neurons; synaptic plasticity; weight variation limits; weights variation interval; Biological neural networks; Biological system modeling; Biomembranes; Brain modeling; Computational modeling; Neurons; bio-inspired neuron model; long-term synaptic plasticity; neuron activity simulation; spike-timing-dependent-plasticity; spiking neurons population;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Theory, Control and Computing (ICSTCC), 2014 18th International Conference
  • Conference_Location
    Sinaia
  • Type

    conf

  • DOI
    10.1109/ICSTCC.2014.6982463
  • Filename
    6982463