Publication Type:

Conference Paper

Source:

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Udupi, India (2017)

URL:

https://ieeexplore.ieee.org/document/8125892/

Keywords:

Adaptation models, adaptive exponential integrate and fire model, Basal ganglia, basal ganglia-cerebellum, Biological system modeling, Brain modeling, Brain models, cerebellar-thalamo-basal ganglia-cortical circuit, cognition, cognitive operations, Computational neuroscience, Diseases, dopamine modulation, fast spiking striatal neuron, Firing, Globus pallidus, Integrated circuit modeling, interconnected brain circuits, medium spiny neuron, motor dysfunction, neural circuit malfunctions, neural nets, Neurons, Neurophysiology, Parkinsons disease, sub thalamic nucleus modelling, substantia nigra circuit, Unsupervised learning

Abstract:

Several interconnected brain circuits such as cerebellum, cerebral cortex, thalamus and basal ganglia process motor information in many species including mammals. Interconnection between basal ganglia and cerebellum through thalamus and cortex may influence the pathways involved in basal ganglia processing. Malfunctions in the neural circuitry of basal ganglia influenced by modifications in the dopaminergic system, which are liable for an array of motor disorders and slighter cognitive issues in Parkinson's disease. Both basal ganglia and cerebellum receives input from and send output to the cerebral cortex and these structures influence motor and cognitive operations through cerebellar-thalamo-basal ganglia-cortical circuit. This interconnected circuit (basal ganglia-cerebellum) helps to understand the role of cerebellum in motor dysfunction during Parkinson's disease. To develop models of unsupervised learning as in brain circuits, we modelled sub thalamic nucleus, internal and external parts of Globus pallidus, fast spiking striatal neuron and medium spiny neuron in striatum using Adaptive Exponential Integrate and Fire model. Simulations highlight the correlation between firing of GPe and level of dopamine and the changes induced during simulated Parkinson's disease. Such models are crucial to understand the motor processing and for developing spiking based deep learning algorithms.

Cite this Research Publication

A. Rajendran, Anuja Thankamani, Nishamol Nirmala, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Computational neuroscience of substantia nigra circuit and dopamine modulation during parkinson's disease”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.