Abstract
In advanced Parkinson's disease (PD), deep brain stimulation (DBS) can be used to disrupt pathological activity in the basal ganglia, thereby reducing PD motor symptoms. The standard protocol for DBS, continuous high frequency stimulation of target cells, is applied notably in subthalamic nucleus (STN) or globus pallidus pars interna. It is proposed that short-duration desynchronizing stimulation protocols may also disrupt pathological synchronous activity [1]. Synaptic plasticity is supposed to be the underlying mechanism. The goal of this study is to explore, with a biophysically plausible model, the role of synaptic plasticity in stabilizing firing patterns in the basal ganglia. Moreover, we investigate how STN stimulation should be applied, such that it exploits synaptic plasticity most effectively to bring the network in a less synchronous state.
Highlights
In advanced Parkinson’s disease (PD), deep brain stimulation (DBS) can be used to disrupt pathological activity in the basal ganglia, thereby reducing PD motor symptoms
We use an existing subthalamic nucleus (STN)-GPe network model consisting of 16 STN and globus pallidus pars externa (GPe) cells mutually connected via a sparse structured architecture [2]
The synaptic weight is updated with an additive nearestspike pair-based STDP rule
Summary
In advanced Parkinson’s disease (PD), deep brain stimulation (DBS) can be used to disrupt pathological activity in the basal ganglia, thereby reducing PD motor symptoms. The standard protocol for DBS, continuous high frequency stimulation of target cells, is applied notably in subthalamic nucleus (STN) or globus pallidus pars interna. It is proposed that short-duration desynchronizing stimulation protocols may disrupt pathological synchronous activity [1]. Synaptic plasticity is supposed to be the underlying mechanism. The goal of this study is to explore, with a biophysically plausible model, the role of synaptic plasticity in stabilizing firing patterns in the basal ganglia. We investigate how STN stimulation should be applied, such that it exploits synaptic plasticity most effectively to bring the network in a less synchronous state
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