Automated Reduction of Detailed Biophysical Cerebellar Neurons to Izhikevich Neurons

In computational neuroscience, the automatic fitting of neuron parameters of simple neurons based on data or other detailed models is a challenging problem. Stochastic variability of firing activity in neurons makes an automated fitting, a complex neuronal dynamic problem. In this study, four features of neuronal firing including spike amplitude, spike count, spike timing, and inter-spike interval were employed to create a simplified neuronal network model using particle swarm optimization and genetic algorithm. This paper showcases a methodology to reduce detailed models that arise from experimental data into simple spiking Izhikevich models, which can be computationally more effective while reconstructing large-scale circuits for behavioral modeling. The approach is illustrated during spontaneous firing activity and current injection to validate the model. The result indicated that the reduced neuronal model shows matching firing activity when optimize with optimization algorithms. The four features of the neuronal activity were matched with the experimental data after optimization. The study also analyzed the parameter fitting accuracy and runtime efficiency of reduction based on the optimization algorithms. The result indicated that the reduction based on particle swarm optimization showed less error percentage while reducing the model when compared with the genetic algorithm.