A computational model of GPe prototypic and arkypallidal neurons with automated parameter fitting.

  • Azevedo Carvalho Nathalie
  • Buhry Laure
  • Contassot-Vivier Sylvain
  • Baufreton Jérôme
  • Martinez Dominique

  • Computational model
  • Neuroscience
  • Parameter fitting
  • GPeA/ GPeP neurons


Parkinson’s disease is characterized by pathological oscillations in the basal ganglia. To gain insight on the origin of these oscillations, we developed a computational model of the globus pallidus (GPe). Our model consists of interconnected prototypic (GPeP) and arkypallidal (GPeA) neurons [1, 5]. We modeled GPeP and GPeA neurons as single-compartment neurons using Hodgkin-Huxley formalism. The GPeA and GPeP neurons have similar ionic currents (I_NaP, I_NaF, I_HCN, I_SK, I_Kv3, I_Ca2+, I_leak) but differ from their conductance values. We tuned the parameters automatically with a multi-objective optimization approach, a variant of the differential evolution [4, 6]. From extensive simulations performed with the SiReNe software (Neural networks simulator, in french: Simulateur de Réseaux de Neurones [3]), we show that our model of GPeP and GPeA neurons are in good agreement with the physiological results of [1], i.e. F-I curves (see Fig. 1A), Voltage-Clamp and I-V relation (see Fig. 1B,C), shape of Action Potentials. Moreover, we show that our GPeP/A neurons interconnected with GABAergic synapses exhibit activity patterns similar to those observed in vivo [2]. This work aims at better understanding the influence of these two different types of neurons in Parkinson’s disease.