Stochastic resonance can induce oscillation in a recurrent Hodgkin-Huxley neuron model with added Gaussian noise.
Stochastic resonance (SR) has been shown to enhance the signal to noise ratio or detection of signals in neurons. It has not been clear yet if SR can play an important role in information processing in neural networks. In this paper, we test the hypothesis through computer simulations that SR can induce an oscillation phenomenon in a recurrent neural network with added Gaussian noise in which the recurrent model is constructed by four Hodgkin-Huxley (HH) neuron models. Each HH neuron model is driven by Gaussian noise and sub-threshold excitatory synaptic currents with an alpha function from another HH neuron model, and the action potentials (spike firings) of each HH neuron model are transferred to the other HH neuron model via sub-threshold synaptic currents. From spike firing times recorded, the inter spike interval (ISI) histogram was generated, and the periodicity of spike firings was detected from the ISI histogram at each HH neuron model. The results show that the probability of spike firings in the oscillation period (about 50 ms or 20 Hz) increases as the standard deviation (S.D.) of the Gaussian white noise increases, and reach a maximum value at a specific S.D. of the Gaussian white noise, implying that SR can improve sub-threshold synaptic transmission in the recurrent HH neuron model. It is concluded that an oscillation (20 Hz) can be induced by adding Gaussian white noise at lower amplitudes with intrinsic characteristics in the recurrent HH neuron model, while another oscillation (100 Hz) can be generated by the noise at greater amplitudes with extrinsic characteristics.
Mino
Hiroyuki
H
Department of Electrical and Computer Engineering, Kanto Gakuin University, Yokohama, Japan. mino@ieee.org
Durand
Dominique M
DM
D000200:Action Potentials; D003198:Computer Simulation; D006801:Humans; D008564:Membrane Potentials; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009474:Neurons / Q000378:metabolism*; D016011:Normal Distribution; D009991:Oscillometry; D011336:Probability; D013269:Stochastic Processes; D009435:Synaptic Transmission; D013997:Time Factors