Publication Type : Conference Paper
Thematic Areas : Learning-Technologies, Medical Sciences, Biotech
Publisher : Proceedings of the International symposium on Translational Neuroscience & XXXII Annual Conference of the Indian Academy of Neurosciences, NIMHANS, Bangalore , India.
Source : Proceedings of the International symposium on Translational Neuroscience & XXXII Annual Conference of the Indian Academy of Neurosciences, NIMHANS, Bangalore , India (2014)
Url : https://www.researchgate.net/publication/294718917_Parallelization_of_Cerebellar_Granular_Layer_Circuitry_Model_for_Physiological_Predictions
Campus : Amritapuri
School : School of Biotechnology
Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology
Department : Computational Neuroscience Laboratory, biotechnology
Year : 2014
Abstract : Background: The cerebellar granular layer input stage of cerebellum receives information from tactile and sensory regions of the body. The somatosensory activity in the cerebellar granular layer corresponds to sensory and tactile input has been observed by recording Local Field Potential (LFP) from the Crus-IIa regions of cerebellum in brain slices and in anesthetized animals.
Purpose: In this paper, a detailed biophysical model of Wistar rat cerebellum granular layer network model and LFP modelling schemas were used to simulate circuit’s evoked response.
Methods: Point Source Approximation and Line Source Approximation were used to reconstruct the network LFP. The LFP mechanism in in vitro was validated in network model and generated the in vivo LFP using the same mechanism.
Results: The network simulations distinctly displayed the Trigeminal and Cortical (TC) wave components generated by 2 independent bursts implicating the generation of TC waves by 2 independent granule neuron populations. Induced plasticity was simulated to estimate granule neuron activation related population responses. As a prediction, cerebellar dysfunction (ataxia) was also studied using the model. Dysfunction at individual neurons in the network was affected by the population response.
Conclusion: Our present study utilizes available knowledge on known mechanisms in a single cell and associates network function to population responses.
Cite this Research Publication : A. Yoosef, Arathi G. Rajendran, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Parallelization of Cerebellar Granular Layer Circuitry Model for Physiological Predictions”, in Proceedings of the International symposium on Translational Neuroscience & XXXII Annual Conference of the Indian Academy of Neurosciences, NIMHANS, Bangalore , India, Nov 1-Nov 3,2014.