Phase Entrainment by Periodic Stimuli In Silico: A Quantitative Study

Abstract

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