Spike-timing-dependent plasticity and random inputs shape interspike interval regularity of model STN neurons

Abstract: Neuronal oscillations are closely related to the symptoms of Parkinson's disease (PD). In this study, we explore how random fluctuations (or "stochastic inputs") affect these oscillations in brain states, which reflect the collective activity of interconnected neurons. These random inputs are modeled in the context of the subthalamic nucleus (STN), a brain region implicated in PD, and their interaction with synaptic dynamics and spike-timing-dependent plasticity (STDP) in both healthy and PD-affected neurons. Specifically, we investigate the effects of random synaptic inputs and their correlations on the membrane potential of STN neurons. Our results show that these random inputs significantly influence the firing patterns of STN neurons, both in healthy cells and in those affected by PD under deep brain stimulation (DBS) treatment. We also find that STDP increases the regularity of the interspike intervals (ISI) in spike trains of output neurons. However, the introduction of random refractory periods and fluctuating input currents can induce greater irregularity in the spike trains. Furthermore, when random inputs and STDP are combined, the correlation between the activity of different neurons increases. These findings suggest that the stochastic dynamics of STN neurons, in conjunction with STDP, could offer insights into the mechanisms underlying PD symptoms and their potential management.