Understanding Cerebellum Granular Layer Network Computations through Mathematical Reconstructions of Evoked Local Field Potentials
Articolo
Data di Pubblicazione:
2018
Citazione:
Understanding Cerebellum Granular Layer Network Computations through Mathematical Reconstructions of Evoked Local Field Potentials / H. Parasuram, B. Nair, G. Naldi, E. D’Angelo, S. Diwakar. - In: ANNALS OF NEUROSCIENCES. - ISSN 0972-7531. - 25:1(2018), pp. 11-24.
Abstract:
Background: The cerebellar granular layer input stage of cerebellum receives information from tactile and sensory re-
gions 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 net-
work model and LFP modelling schemas were used to simulate circuit’s evoked response. Methods: Point Source Ap-
proximation 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.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Local field potentials; Cerebellar granular layer; Plasticity; Computational neuroscience
Elenco autori:
H. Parasuram, B. Nair, G. Naldi, E. D’Angelo, S. Diwakar
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