Hindmarsh-Rose neuronal network with spike-timing-dependent plasticity demonstrates coordinated reset neuromodulation for Parkinson's disease

Computational models of brain structures impacted by Parkinson's disease are useful for studying pathological synchronization and exploring potential therapies. We use the Hindmarsh-Rose neuronal model to simulate synchronized activity in the subthalamic nucleus, capturing key features of the pathological rhythms associated with parkinsonian tremor. Our model incorporates chemical synapses whose strengths evolve according to a spike-timing-dependent plasticity (STDP) rule. We apply coordinated reset stimulation with rapidly varying sequences (RVS CR) and examine its ability to weaken synaptic weights, thereby reducing neuronal synchrony. This stimulation technique delivers phase-shifted stimuli to distinct sites in a random sequence. We explore how stimulation frequency and the number of stimulation sites affect the efficacy of RVS CR at desynchronizing the network and observe good agreement with previous studies. We demonstrate that RVS CR efficacy is sensitive to the depression-to-potentiation ratio in the STDP rule, which may be an important parameter to tune when reconciling simulations with experimental data. Numerical simulation of neuronal networks is constrained by computational resources when models demand large networks. This work proposes a model that demonstrates similar utility with a relatively small network, enabling researchers to study pathological neuronal activity and treatments more efficiently.