Modeling Spike-Train Processing in the Cerebellum Granular Layer and Changes in Plasticity Reveal Single Neuron Effects in Neural Ensembles
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 sim...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463164/ https://www.ncbi.nlm.nih.gov/pubmed/23193390 http://dx.doi.org/10.1155/2012/359529 |
_version_ | 1782245266235064320 |
---|---|
author | Medini, Chaitanya Nair, Bipin D'Angelo, Egidio Naldi, Giovanni Diwakar, Shyam |
author_facet | Medini, Chaitanya Nair, Bipin D'Angelo, Egidio Naldi, Giovanni Diwakar, Shyam |
author_sort | Medini, Chaitanya |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3463164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-34631642012-11-28 Modeling Spike-Train Processing in the Cerebellum Granular Layer and Changes in Plasticity Reveal Single Neuron Effects in Neural Ensembles Medini, Chaitanya Nair, Bipin D'Angelo, Egidio Naldi, Giovanni Diwakar, Shyam Comput Intell Neurosci Research Article 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. Hindawi Publishing Corporation 2012 2012-09-25 /pmc/articles/PMC3463164/ /pubmed/23193390 http://dx.doi.org/10.1155/2012/359529 Text en Copyright © 2012 Chaitanya Medini et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Medini, Chaitanya Nair, Bipin D'Angelo, Egidio Naldi, Giovanni Diwakar, Shyam Modeling Spike-Train Processing in the Cerebellum Granular Layer and Changes in Plasticity Reveal Single Neuron Effects in Neural Ensembles |
title | Modeling Spike-Train Processing in the Cerebellum Granular Layer
and Changes in Plasticity Reveal Single Neuron Effects in Neural
Ensembles |
title_full | Modeling Spike-Train Processing in the Cerebellum Granular Layer
and Changes in Plasticity Reveal Single Neuron Effects in Neural
Ensembles |
title_fullStr | Modeling Spike-Train Processing in the Cerebellum Granular Layer
and Changes in Plasticity Reveal Single Neuron Effects in Neural
Ensembles |
title_full_unstemmed | Modeling Spike-Train Processing in the Cerebellum Granular Layer
and Changes in Plasticity Reveal Single Neuron Effects in Neural
Ensembles |
title_short | Modeling Spike-Train Processing in the Cerebellum Granular Layer
and Changes in Plasticity Reveal Single Neuron Effects in Neural
Ensembles |
title_sort | modeling spike-train processing in the cerebellum granular layer
and changes in plasticity reveal single neuron effects in neural
ensembles |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463164/ https://www.ncbi.nlm.nih.gov/pubmed/23193390 http://dx.doi.org/10.1155/2012/359529 |
work_keys_str_mv | AT medinichaitanya modelingspiketrainprocessinginthecerebellumgranularlayerandchangesinplasticityrevealsingleneuroneffectsinneuralensembles AT nairbipin modelingspiketrainprocessinginthecerebellumgranularlayerandchangesinplasticityrevealsingleneuroneffectsinneuralensembles AT dangeloegidio modelingspiketrainprocessinginthecerebellumgranularlayerandchangesinplasticityrevealsingleneuroneffectsinneuralensembles AT naldigiovanni modelingspiketrainprocessinginthecerebellumgranularlayerandchangesinplasticityrevealsingleneuroneffectsinneuralensembles AT diwakarshyam modelingspiketrainprocessinginthecerebellumgranularlayerandchangesinplasticityrevealsingleneuroneffectsinneuralensembles |