Publication Type:

Book Chapter

Source:

Mathematical and Theoretical Neuroscience: Cell, Network and Data Analysis, Springer International Publishing, Cham, p.61–85 (2018)

ISBN:

9783319682976

URL:

https://doi.org/10.1007/978-3-319-68297-6_4

Abstract:

Recent studies show cerebellum having a crucial role in motor coordination and cognition, and it has been observed that in patients with movement disorders and other neurological conditions cerebellar circuits are known to be affected. Simulations allow insight on how cerebellar granular layer processes spike information and to understand afferent information divergence in the cerebellar cortex. With excitation-inhibition ratios adapted from in vitro experimental data in the cerebellum granular layer, the model allows reconstructing spatial recoding of sensory and tactile patterns in cerebellum. Granular layer population activity reconstruction was performed with biophysical modeling of fMRI BOLD signals and evoked local field potentials from single neuron and network models implemented in NEURON environment. In this chapter, evoked local field potentials have been reconstructed using biophysical and neuronal mass models interpreting averaged activity and constraining population behavior as observed in experiments. Using neuronal activity and correlating blood flow using the balloon and modified Windkessel model, generated cerebellar granular layer BOLD response. With the focus of relating neural activity to clinical correlations such models help constraining network models and predicting activity-dependent emergent behavior and manifestations. To reverse engineering brain function, cerebellar circuit functions were abstracted into a spiking network based trajectory control model for robotic articulation.

Cite this Research Publication

S. Diwakar, Nutakki, C., Bodda, S., Rajendran, A., Vijayan, A., and Dr. Bipin G. Nair, “Mathematical Modelling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions”, in Mathematical and Theoretical Neuroscience: Cell, Network and Data Analysis, G. Naldi and Nieus, T. Cham: Springer International Publishing, 2018, pp. 61–85.

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