Building Neurocomputational Models at Different Levels for Basal Ganglia Circuit

Abstract

 The target of computational neuroscience studies can be considered in two-folds: understanding the connection between the physiology and functional aspects of the brain to develop new approaches for diagnosing and treatment of neurological disorders and behavioral deficits and understanding mind and consciousness to develop new intelligent technologies. The methods and approaches used in computational neuroscience have to overcome the complexity of the system in all aspects. So, different methods and approaches are developed for different scales not only for observing the phenomena, but also for modeling. In this paper, an approach is proposed to build a connection between different levels of modeling. A simple, linear system will be shown to give an understanding of the working principle of basal ganglia circuit which is modeled with a detailed spiking neural network approach. First, spiking neural network of basal ganglia circuit will be introduced and the role of dopamine on its functioning will be shown; then a simple linear system model will be given, and the relation between two models will be explained.  The aim of this work is to show that even a simple model which is not sufficient for detailed understanding of the neuronal process, could give a coarse understanding of a complex phenomenon. Such simple models could be used as a starting point in building complex models and also can be benefited for implementing intelligent technologies.

Keywords

• basal ganglia circuit,spiking neural network,mass model

References

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