Phase entrainment by periodic stimuli in silico: A quantitative study

We present a quantitative study of phase entrainment by periodic visual stimuli in biologically inspired neural network. The objective is to understand the neuronal population dynamics that underlie brain oscillations external stimuli, which used for therapeutic treatment neurological disorders, example Parkinsonian tremor. Yet, underpinning such not fully understood. Rhythmic sensory stimulation one way studying synchronization brain. A recent experimental has reported during steady state visually evoked potentials (SSVEP), are scalp electroencephalogram corresponding stimuli. have simulated SSVEP-like signals pulse input our silico model. locking values, normalised Shannon entropy and conditional probability as synchronisation indices show synchrony populations. Our experiment demonstrates disappears with jitter inter-pulse intervals, this would be case if output signal were superposition responses different signals. Thus, implies network response input. Overall, shows plausibility using models, validated works, make testable predictions on specific disorders.