Development and validation of a neural population model based on the dynamics of a discontinuous membrane potential neuron model

Using a hybrid neuron in physiologically inspired models of the basal ganglia

Our current understanding of the basal ganglia (BG) has facilitated the creation of computational models that have contributed novel theories, explored new functional anatomy and demonstrated results complementing physiological experiments. However, the utility of these models extends beyond these applications. Particularly in neuromorphic engineering, where the basal ganglia's role in computation is important for applications such as power efficient autonomous agents and model-based control strategies. The neurons used in existing computational models of the BG, however, are not amenable for many low-power hardware implementations. Motivated by a need for more hardware accessible networks, we replicate four published models of the BG, spanning single neuron and small networks, replacing the more computationally expensive neuron models with an Izhikevich hybrid neuron. This begins with a network modeling action-selection, where the basal activity levels and the ability to appropriately select the most salient input is reproduced. A Parkinson's disease model is then explored under normal conditions, Parkinsonian conditions and during subthalamic nucleus deep brain stimulation (DBS). The resulting network is capable of replicating the loss of thalamic relay capabilities in the Parkinsonian state and its return under DBS. This is also demonstrated using a network capable of action-selection. Finally, a study of correlation transfer under different patterns of Parkinsonian activity is presented. These networks successfully captured the significant results of the originals studies. This not only creates a foundation for neuromorphic hardware implementations but may also support the development of large-scale biophysical models. The former potentially providing a way of improving the efficacy of DBS and the latter allowing for the efficient simulation of larger more comprehensive networks.

Dynamics of the subthalamo-pallidal complex in Parkinson's disease during deep brain stimulation

The dynamics of the subthalamo-pallidal complex in Parkinson's disease during deep brain stimulation (DBS) were studied using two models, a simple firing-rate model and a population-based model. We extended the simple firing-rate model of the complex formed by the subthalamic nucleus (STN) and the external segment of the Globus Pallidus (GPe) to explore its dynamical regime during DBS. More specifically, the modulation of neuronal activity (i.e., pattern and amplitude) during DBS was studied. A similar approach was used with the population-based model. Simulation results revealed a gradual decrease in bursting activity in STN cells when the DBS frequency increased. In addition, the contribution of the stimulation current type (mono- or biphasic) to the results was also examined. A comparison of the two models indicated that the population-based model was more biologically realistic and more appropriate for exploring DBS mechanisms. Understanding the underlying mechanisms of DBS is a prerequisite for developing new stimulation protocols.

New insights offered by a computational model of deep brain stimulation

Deep brain stimulation (DBS) is a standard neurosurgical procedure used to treat motor symptoms in about 5% of patients with Parkinson's disease (PD). Despite the indisputable success of this procedure, the biological mechanisms underlying the clinical benefits of DBS have not yet been fully elucidated. The paper starts with a brief review on the use of DBS to treat PD symptoms. The second section introduces a computational model based on the population density approach and the Izhikevich neuron model. We explain why this model is appropriate for investigating macroscopic network effects and exploring the physiological mechanisms which respond to this treatment strategy (i.e., DBS). Finally, we present new insights into the ways this computational model may help to elucidate the dynamic network effects produced in a cerebral structure when DBS is applied.