Spike Timing-Dependent Plasticity and Random Inputs Shape Interspike Interval Regularity of Model STN Neurons
<b>Background/Objectives:</b> Neuronal oscillations play a key role in the symptoms of Parkinson’s disease (PD). This study investigates the effects of random synaptic inputs, their correlations, and the interaction with synaptic dynamics and spike timing-dependent plasticity (STDP) on t...
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2025-07-01
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| author | Thoa Thieu Roderick Melnik |
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| description | <b>Background/Objectives:</b> Neuronal oscillations play a key role in the symptoms of Parkinson’s disease (PD). This study investigates the effects of random synaptic inputs, their correlations, and the interaction with synaptic dynamics and spike timing-dependent plasticity (STDP) on the membrane potential and firing patterns of subthalamic nucleus (STN) neurons, both in healthy and PD-affected states. <b>Methods:</b> We used a modified Hodgkin–Huxley model with a Langevin stochastic framework to study how synaptic conductance, random input fluctuations, and STDP affect STN neuron firing and membrane potential, including sensitivity to refractory period and synaptic depression variability. <b>Results:</b> Our results show that random inputs significantly affect the firing patterns of STN neurons, both in healthy cells and those with PD under DBS treatment. STDP, along with random refractory periods and fluctuating input currents, increases the irregularity of inter-spike intervals (ISIs) in output neuron spike trains. Sensitivity analyses highlight the key role of synaptic depression and refractory period variability in shaping firing patterns. Combining random inputs with STDP boosts the correlation between neuron activities. Furthermore, at fixed input noise levels, the model’s output closely matches experimental firing rate and ISI variability data from PD patients and animals, with statistical tests confirming significant effects of STDP on firing regularity. <b>Conclusions:</b> The findings suggest that the stochastic dynamics of STN neurons, combined with STDP, are crucial for shaping neuronal firing patterns in both healthy and PD-affected states. These insights improve our understanding of how noise and plasticity contribute to neural function and dysfunction, with implications for PD symptom management. |
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| language | English |
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| spelling | doaj-art-b4db6862c6b04822a43d6e8f20b765f12025-07-25T13:15:56ZengMDPI AGBiomedicines2227-90592025-07-01137171810.3390/biomedicines13071718Spike Timing-Dependent Plasticity and Random Inputs Shape Interspike Interval Regularity of Model STN NeuronsThoa Thieu0Roderick Melnik1School of Mathematical and Statistical Science, College of Health Professions, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USAMS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada<b>Background/Objectives:</b> Neuronal oscillations play a key role in the symptoms of Parkinson’s disease (PD). This study investigates the effects of random synaptic inputs, their correlations, and the interaction with synaptic dynamics and spike timing-dependent plasticity (STDP) on the membrane potential and firing patterns of subthalamic nucleus (STN) neurons, both in healthy and PD-affected states. <b>Methods:</b> We used a modified Hodgkin–Huxley model with a Langevin stochastic framework to study how synaptic conductance, random input fluctuations, and STDP affect STN neuron firing and membrane potential, including sensitivity to refractory period and synaptic depression variability. <b>Results:</b> Our results show that random inputs significantly affect the firing patterns of STN neurons, both in healthy cells and those with PD under DBS treatment. STDP, along with random refractory periods and fluctuating input currents, increases the irregularity of inter-spike intervals (ISIs) in output neuron spike trains. Sensitivity analyses highlight the key role of synaptic depression and refractory period variability in shaping firing patterns. Combining random inputs with STDP boosts the correlation between neuron activities. Furthermore, at fixed input noise levels, the model’s output closely matches experimental firing rate and ISI variability data from PD patients and animals, with statistical tests confirming significant effects of STDP on firing regularity. <b>Conclusions:</b> The findings suggest that the stochastic dynamics of STN neurons, combined with STDP, are crucial for shaping neuronal firing patterns in both healthy and PD-affected states. These insights improve our understanding of how noise and plasticity contribute to neural function and dysfunction, with implications for PD symptom management.https://www.mdpi.com/2227-9059/13/7/1718activity-dependent development of nervous systemsspike timing-dependent plasticitycoupled models in medical applicationsneuromorphic systemsneurodegenerative diseasesenhanced Hodgkin–Huxley models |
| spellingShingle | Thoa Thieu Roderick Melnik Spike Timing-Dependent Plasticity and Random Inputs Shape Interspike Interval Regularity of Model STN Neurons Biomedicines activity-dependent development of nervous systems spike timing-dependent plasticity coupled models in medical applications neuromorphic systems neurodegenerative diseases enhanced Hodgkin–Huxley models |
| title | Spike Timing-Dependent Plasticity and Random Inputs Shape Interspike Interval Regularity of Model STN Neurons |
| title_full | Spike Timing-Dependent Plasticity and Random Inputs Shape Interspike Interval Regularity of Model STN Neurons |
| title_fullStr | Spike Timing-Dependent Plasticity and Random Inputs Shape Interspike Interval Regularity of Model STN Neurons |
| title_full_unstemmed | Spike Timing-Dependent Plasticity and Random Inputs Shape Interspike Interval Regularity of Model STN Neurons |
| title_short | Spike Timing-Dependent Plasticity and Random Inputs Shape Interspike Interval Regularity of Model STN Neurons |
| title_sort | spike timing dependent plasticity and random inputs shape interspike interval regularity of model stn neurons |
| topic | activity-dependent development of nervous systems spike timing-dependent plasticity coupled models in medical applications neuromorphic systems neurodegenerative diseases enhanced Hodgkin–Huxley models |
| url | https://www.mdpi.com/2227-9059/13/7/1718 |
| work_keys_str_mv | AT thoathieu spiketimingdependentplasticityandrandominputsshapeinterspikeintervalregularityofmodelstnneurons AT roderickmelnik spiketimingdependentplasticityandrandominputsshapeinterspikeintervalregularityofmodelstnneurons |