Model analysis of the frequency of neuronal action potentials in the case of Gaussian input stochastic processes.
The authors describe the effects of changes in excitatory and inhibitory synapse weight on the number of spikes generated in the presence of constant absolute refractory phase and threshold level values. Input stochastic processes with Gaussian distributions were presumed. The problem was resolved in a hybrid computer model of stochastic neuronal activity. The results are given in the form of "e-curves", "i-curves" and gradient fields. It was shown that, in a set of paired values of the two weights, zones could be found in which the number of generated spikes depended mainly on just one of them. It was also shown that, in a given neurone with a fixed synapse morphology, the effect of the individual synapses on the number of generated spikes altered with changes in input stochastic processes to the synapses.
D000200:Action Potentials; D003203:Computers, Hybrid; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D011336:Probability; D013269:Stochastic Processes