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
Link To Document :
بازگشت