Open AccessSimulation of electric activity of neuron by setting up a reliable neuronal circuit driven by electric autapse
Author(s) -
Guo-Dong Ren,
Gang Wu,
Jun Ma,
Chen Yang
Publication year - 2015
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.64.058702
Subject(s) - electric fish , bursting , physics , electronic circuit , voltage , biological neuron model , electric field , electric potential , electric current , neuron , computer science , neuroscience , control theory (sociology) , biology , quantum mechanics , artificial intelligence , fishery , fish <actinopterygii> , control (management)
Transition of electric activity of neuron can be induced by electric autapse, and its action potential is much sensitive to the stimuli from the electric autapse. Generally, the effect of electric autapse on membrane potential of neuron is often described by using time-delayed feedback in closed loop. Based on Pspice software, a class of electric circuit is designed with the electric autapse being taken into consideration, and a time-delayed circuit is used to detect the adjusting action of electric autapse on the action potential. Results are found as follows: (1) The neuronal electric circuit can produce quiescent state, spiking, bursting state under an external force besides the electric autapse circuit. (2) The transition of electric activity occurs between four different atates (quiescent, spiking, bursting state) by imposing a time-varying forcing current; its potential mechanism is that the electric circuit is associated with the memory, and the neuron can give different types of response to the same external forcing current. (3)When a strong external force is imposed, the outputs can show different type of electric activities due to an electric autapse, that is to say, self-adaption of gain in the autapse is useful for the neuron and thus different type of electric activities occurs, whose potential mechanism may be due to the effective feedback in the loop; so it is helpful to understand the synaptic plasticity.