Publication Type:

Conference Paper

Source:

Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), IEEE, Bangalore, Karnataka, India (2018)

URL:

https://ieeexplore.ieee.org/document/8554491

Keywords:

cerebellum, Computational neuroscience, deep cerebellar nuclei, Mathematical modeling

Abstract:

Deep cerebellar nuclei (DCN) are central neurons attributed to determining the modulation in descending motor systems and considered as the final integrators of cerebellar information. A multi-compartmental morphologically realistic model of a DCN neuron was mathematically reconstructed with active ion channels as part of this study. The effect of inhibition from Purkinje neurons controls the excitatory outcome from a DCN. As a preliminary study, the spontaneous firing of DCN was reconstructed and other firing patterns were validated by applying current pulses using current clamp protocol. Effects of inhibition on constant excitation were analyzed to understand the modulation of firing properties of DCN. Simulations demonstrate that the inhibitory input can alter the temporal patterns in DCN and could modify sensory-tactile and other signals to interconnected motor circuits.

Cite this Research Publication

Presannan A, Rajendran A., Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Reproducing the firing properties of a cerebellum deep cerebellar nucleus with a multi compartmental morphologically realistic biophysical model”, in Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), Bangalore, Karnataka, India, 2018.