Publication Type : Journal Article
Thematic Areas : Learning-Technologies, Medical Sciences, Biotech
Publisher : Computational Intelligence and Neuroscience
Source : Computational Intelligence and Neuroscience, Volume 2012 (2012), Article ID 359529.
Url : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463164/
Campus : Amritapuri
School : School of Biotechnology
Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology
Department : biotechnology
Year : 2012
Abstract : The cerebellum input stage has been known to perform combinatorial operations on input signals. In this paper, two types of mathematical models were used to reproduce the role of feed-forward inhibition and computation in the granular layer microcircuitry to investigate spike train processing. A simple spiking model and a biophysically-detailed model of the network were used to study signal recoding in the granular layer and to test observations like center-surround organization and time-window hypothesis in addition to effects of induced plasticity. Simulations suggest that simple neuron models may be used to abstract timing phenomenon in large networks, however detailed models were needed to reconstruct population coding via evoked local field potentials (LFP) and for simulating changes in synaptic plasticity. Our results also indicated that spatio-temporal code of the granular network is mainly controlled by the feed-forward inhibition from the Golgi cell synapses. Spike amplitude and total number of spikes were modulated by LTP and LTD. Reconstructing granular layer evoked-LFP suggests that granular layer propagates the nonlinearities of individual neurons. Simulations indicate that granular layer network operates a robust population code for a wide range of intervals, controlled by the Golgi cell inhibition and is regulated by the post-synaptic excitability.
Cite this Research Publication : Medini C, Nair B, D'Angelo E, Naldi G, Diwakar S, Modeling spike-train processing in the cerebellum
granular layer and changes in plasticity reveals single neuron effects in neural ensembles,
Computational Intelligence and Neuroscience, Volume 2012 (2012), Article ID 359529