  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 of
brain oscillations by external stimuli, which is used for therapeutic treatment
in neurological disorders, for example in Parkinsonian tremor. Yet, the
neuronal dynamics underpinning such entrainment is not fully understood.
Rhythmic sensory stimulation is one way of studying phase synchronization in
the brain. A recent experimental study has reported phase entrainment of brain
oscillations during steady state visually evoked potentials (SSVEP), which are
scalp electroencephalogram corresponding to periodic 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 to show phase synchrony in
the neuronal populations. Our experiment demonstrates that the phase
synchronisation disappears with jitter in the input inter-pulse intervals, and
this would not be the case if the output signal were to be the superposition of
the responses to the different input signals. Thus, 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 by experimental works, to understand and make testable predictions on
brain entrainment as a therapeutic treatment in specific neurological
disorders.
