Development and Analysis of a Spiking Neural Network Description of the STN-GPe Loop
Investigation of parameter dependencies and critical behavior, in particular oscillations, of a spiking neural network describing the interaction between the subthalamic nucleus (STN) and the globus pallidus pars externa (GPe).
The loop between STN and GPe stands out as the major coupled excitatory-inhibitory (E-I) system inside the basal ganglia. In PD, increased neural synchronization and coherence has been found in both STN and GPe [2]. Furthermore, deep brain stimulation at both sites has been able to counteract Parkinsonian symptoms successfully. This has led to the hypothesis that the STN-GPe loop acts as a generator of beta oscillations in PD due to its E-I properties [3]. In this project, we develop a mean-field model of neural spiking activities in STN and GPe (see Fig. 1). By means of bifurcation and sensitivity analysis (using PyRates [1] and pygpc [4], respectively), we investigate parameter dependencies of our model. Specifically, we examine the critical dependency of oscillations in the STN-GPe system on the properties of synaptic transmission. Furthermore, we systematically test the generalization of our mean-field model to spiking neural networks with different network size, coupling density, and neuron types. Thus, we can make explicit statements for which types of spiking neural networks our findings hold.
Figure 1: Structure of the STN-GPe model.
[1] Gast R, Rose D, Salomon C, Möller HE, Weiskopf N, Knösche TR. PyRates— A Python framework for rate-based neural simulations. PLOS ONE.2019; 14(12): e0225900. doi:10.1371/journal.pone.0225900.
[2] Wichmann T, Soares J. Neuronal Firing Before and After Burst Discharges in the Monkey Basal Ganglia Is Predictably Patterned in the Normal State and Altered in Parkinsonism. Journal of Neurophysiology. 2006; 95(4): 2120-2133.
[3] Pavlides A, Hogan SJ, Bogacz R. Computational Models Describing Possible Mechanisms for Generation of excessive Beta Oscillations in Parkinson’s Disease. PLoS Computational Biology. 2015; 11(12).
[4] Weise K, Poßner L, Müller E, Richard G, Knösche TR. Pygpc: A sensitivity and uncertainty analysis toolbox for Python. SoftwareX. 2020; 11: 100450.