A Network Model of Perceptual Suppression Induced by Transcranial Magnetic Stimulation

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Abstract
We modeled the inhibitory effects of transcranial magnetic stimulation (TMS) on a neural population. TMS is a noninvasive technique, with high temporal resolution, that can stimulate the brain via a brief magnetic pulse from a coil placed on the scalp. Because of these advantages, TMS is extensively used as a powerful tool in experimental studies of motor, perception, and other functions in humans. However, the mechanisms by which TMS interferes with neural activities, especially in terms of theoretical aspects, are totally unknown. In this study, we focused on the temporal properties of TMS-induced perceptual suppression, and we computationally analyzed the response of a simple network model of a sensory feature detector system to a TMS-like perturbation. The perturbation caused the mean activity to transiently increase and then decrease for a long period, accompanied by a loss in the degree of activity localization. When the afferent input consisted of a dual phase, with a strong transient component and a weak sustained component, there was a critical latency period of the perturbation during which the network activity was completely suppressed and converged to the resting state. The range of the suppressive period increased with decreasing afferent input intensity and reached more than 10 times the time constant of the neuron. These results agree well with typical experimental data for occipital TMS and support the conclusion that dynamical interaction in a neural population plays an important role in TMS-induced perceptual suppression.