Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Computational models of timing mechanisms in the cerebellar granular layer.

ABSTRACT: A long-standing question in neuroscience is how the brain controls movement that requires precisely timed muscle activations. Studies using Pavlovian delay eyeblink conditioning provide good insight into this question. In delay eyeblink conditioning, which is believed to involve the cerebellum, a subject learns an interstimulus interval (ISI) between the onsets of a conditioned stimulus (CS) such as a tone and an unconditioned stimulus such as an airpuff to the eye. After a conditioning phase, the subject's eyes automatically close or blink when the ISI time has passed after CS onset. This timing information is thought to be represented in some way in the cerebellum. Several computational models of the cerebellum have been proposed to explain the mechanisms of time representation, and they commonly point to the granular layer network. This article will review these computational models and discuss the possible computational power of the cerebellum.

SUBMITTER: Yamazaki T

PROVIDER: S-EPMC2788136 | biostudies-other | 2009 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer.

Project description:The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. Patterned mossy fiber activity induces rhythmic Golgi cell activity that is synchronized by shared parallel fiber input and by gap junctions. This leads to long distance synchrony of Golgi cells along the transverse axis, powerfully regulating granule cell firing by imposing inhibition during a specific time window. The essential network mechanisms, including tunable Golgi cell oscillations, on-beam inhibition and NMDA receptors causing first winner keeps winning of granule cells, illustrate how fundamental properties of the granule layer operate in tandem to produce (1) well timed and spatially bound output, (2) a wide dynamic range of granule cell firing and (3) transient and coherent gating oscillations. These results substantially enrich our understanding of granule cell layer processing, which seems to promote spatial group selection of granule cell activity as a function of timing of mossy fiber input.

| S-EPMC5626500 | biostudies-literature

A Hybrid Model for the Computationally-Efficient Simulation of the Cerebellar Granular Layer.

Project description:The aim of the present paper is to efficiently describe the membrane potential dynamics of neural populations formed by species having a high density difference in specific brain areas. We propose a hybrid model whose main ingredients are a conductance-based model (ODE system) and its continuous counterpart (PDE system) obtained through a limit process in which the number of neurons confined in a bounded region of the brain tissue is sent to infinity. Specifically, in the discrete model, each cell is described by a set of time-dependent variables, whereas in the continuum model, cells are grouped into populations that are described by a set of continuous variables. Communications between populations, which translate into interactions among the discrete and the continuous models, are the essence of the hybrid model we present here. The cerebellum and cerebellum-like structures show in their granular layer a large difference in the relative density of neuronal species making them a natural testing ground for our hybrid model. By reconstructing the ensemble activity of the cerebellar granular layer network and by comparing our results to a more realistic computational network, we demonstrate that our description of the network activity, even though it is not biophysically detailed, is still capable of reproducing salient features of neural network dynamics. Our modeling approach yields a significant computational cost reduction by increasing the simulation speed at least 270 times. The hybrid model reproduces interesting dynamics such as local microcircuit synchronization, traveling waves, center-surround, and time-windowing.

| S-EPMC4837690 | biostudies-literature

Astroglial cells in the external granular layer are precursors of cerebellar granule neurons in neonates.

Project description:It is well established that cerebellar granule cell precursors (GCPs) initially derive from progenitors in the rhombic lip of the embryonic cerebellar primordium. GCPs proliferate and migrate tangentially across the cerebellum to form the external granule cell layer (EGL) in late embryogenesis and early postnatal development. It is unclear whether GCPs are specified exclusively in the embryonic rhombic lip or whether their precursor persists in the neonate. Using transgenic mice expressing DsRed under the human glial fibrillary acidic protein (hGFAP) promoter, we found 2 populations of DsRed(+) cells in the EGL in the first postnatal week defined by bright and faint DsRed-fluorescent signal. Bright DsRed(+) cells have a protein expression profile and electrophysiological characteristics typical of astrocytes, but faint DsRed(+) cells in the EGL and internal granule cell layer (IGL) express markers and physiological properties of immature neurons. To determine if these astroglial cells gave rise to GCPs, we genetically tagged them with EGFP or betagal reporter genes at postnatal day (P)3-P5 using a hGFAP promoter driven inducible Cre recombinase. We found that GFAP promoter(+) cells in the EGL are proliferative and express glial and neural stem cell markers. In addition, immature granule cells (GCs) en route to the IGL at P12 as well as GCs in the mature cerebellum, 30days after recombination, express the reporter protein, suggesting that GFAP promoter(+) cells in the EGL generate a subset of granule cells. The identification of glial cells which function as neuronal progenitor cells profoundly impacts our understanding of cellular plasticity in the developing cerebellum.

| S-EPMC2900521 | biostudies-literature

Expression analysis of cerebellar granular cell layer isolated from PTEN conditional null mutants and controls

Project description:A neuronal PI(3,4,5)P3-dependent program of oligodendrocyte precursor recruitment and myelination was identified in mice that conditionally lack PTEN in cerebellar granular cells (PTEN cKO) Expression analysis was performed with RNA obtained selectively from cerebellar granular cell layer of PTEN conditional null mutants and controls

2016-09-30 | GSE80966 | GEO

Transcription profiling of rat hippocampal CA1 and CA3, cerebral cortex, and cerebellar granular layer brain regions

Project description:Study on selective vulnerability of certain brain regions to oxidative stress. Here we selected 4 brain regions (hippocampal CA1 and CA3, cerebral cortex, and cerebellar granular layer) to study this phenomenon. Experiment Overall Design: Neurons were collected from the 4 regions of the rat brain and subjected to Affymetrix RAE230A analysis, in order to identify genes related to the differential vulnerability of the neurons to oxidative stress.

2008-04-07 | E-GEOD-7139 | biostudies-arrayexpress

Stimulus-dependent state transition between synchronized oscillation and randomly repetitive burst in a model cerebellar granular layer.

Project description:Information processing of the cerebellar granular layer composed of granule and Golgi cells is regarded as an important first step toward the cerebellar computation. Our previous theoretical studies have shown that granule cells can exhibit random alternation between burst and silent modes, which provides a basis of population representation of the passage-of-time (POT) from the onset of external input stimuli. On the other hand, another computational study has reported that granule cells can exhibit synchronized oscillation of activity, as consistent with observed oscillation in local field potential recorded from the granular layer while animals keep still. Here we have a question of whether an identical network model can explain these distinct dynamics. In the present study, we carried out computer simulations based on a spiking network model of the granular layer varying two parameters: the strength of a current injected to granule cells and the concentration of Mg²? which controls the conductance of NMDA channels assumed on the Golgi cell dendrites. The simulations showed that cells in the granular layer can switch activity states between synchronized oscillation and random burst-silent alternation depending on the two parameters. For higher Mg²? concentration and a weaker injected current, granule and Golgi cells elicited spikes synchronously (synchronized oscillation state). In contrast, for lower Mg²? concentration and a stronger injected current, those cells showed the random burst-silent alternation (POT-representing state). It is suggested that NMDA channels on the Golgi cell dendrites play an important role for determining how the granular layer works in response to external input.

| S-EPMC3136428 | biostudies-literature

Optogenetic stimulation of complex spatio-temporal activity patterns by acousto-optic light steering probes cerebellar granular layer integrative properties.

Project description:Optogenetics provides tools to control afferent activity in brain microcircuits. However, this requires optical methods that can evoke asynchronous and coordinated activity within neuronal ensembles in a spatio-temporally precise way. Here we describe a light patterning method, which combines MHz acousto-optic beam steering and adjustable low numerical aperture Gaussian beams, to achieve fast 2D targeting in scattering tissue. Using mossy fiber afferents to the cerebellar cortex as a testbed, we demonstrate single fiber optogenetic stimulation with micron-scale lateral resolution, >100?µm depth-penetration and 0.1?ms spiking precision. Protracted spatio-temporal patterns of light delivered by our illumination system evoked sustained asynchronous mossy fiber activity with excellent repeatability. Combining optical and electrical stimulations, we show that the cerebellar granular layer performs nonlinear integration, whereby sustained mossy fiber activity provides a permissive context for the transmission of salient inputs, enriching combinatorial views on mossy fiber pattern separation.

| S-EPMC6137064 | biostudies-literature

Cerebellar cortex granular layer interneurons in the macaque monkey are functionally driven by mossy fiber pathways through net excitation or inhibition.

Project description:The granular layer is the input layer of the cerebellar cortex. It receives information through mossy fibers, which contact local granular layer interneurons (GLIs) and granular layer output neurons (granule cells). GLIs provide one of the first signal processing stages in the cerebellar cortex by exciting or inhibiting granule cells. Despite the importance of this early processing stage for later cerebellar computations, the responses of GLIs and the functional connections of mossy fibers with GLIs in awake animals are poorly understood. Here, we recorded GLIs and mossy fibers in the macaque ventral-paraflocculus (VPFL) during oculomotor tasks, providing the first full inventory of GLI responses in the VPFL of awake primates. We found that while mossy fiber responses are characterized by a linear monotonic relationship between firing rate and eye position, GLIs show complex response profiles characterized by "eye position fields" and single or double directional tunings. For the majority of GLIs, prominent features of their responses can be explained by assuming that a single GLI receives inputs from mossy fibers with similar or opposite directional preferences, and that these mossy fiber inputs influence GLI discharge through net excitatory or inhibitory pathways. Importantly, GLIs receiving mossy fiber inputs through these putative excitatory and inhibitory pathways show different firing properties, suggesting that they indeed correspond to two distinct classes of interneurons. We propose a new interpretation of the information flow through the cerebellar cortex granular layer, in which mossy fiber input patterns drive the responses of GLIs not only through excitatory but also through net inhibitory pathways, and that excited and inhibited GLIs can be identified based on their responses and their intrinsic properties.

| S-EPMC3869689 | biostudies-literature

Cerebellar Golgi cell models predict dendritic processing and mechanisms of synaptic plasticity.

Project description:The Golgi cells are the main inhibitory interneurons of the cerebellar granular layer. Although recent works have highlighted the complexity of their dendritic organization and synaptic inputs, the mechanisms through which these neurons integrate complex input patterns remained unknown. Here we have used 8 detailed morphological reconstructions to develop multicompartmental models of Golgi cells, in which Na, Ca, and K channels were distributed along dendrites, soma, axonal initial segment and axon. The models faithfully reproduced a rich pattern of electrophysiological and pharmacological properties and predicted the operating mechanisms of these neurons. Basal dendrites turned out to be more tightly electrically coupled to the axon initial segment than apical dendrites. During synaptic transmission, parallel fibers caused slow Ca-dependent depolarizations in apical dendrites that boosted the axon initial segment encoder and Na-spike backpropagation into basal dendrites, while inhibitory synapses effectively shunted backpropagating currents. This oriented dendritic processing set up a coincidence detector controlling voltage-dependent NMDA receptor unblock in basal dendrites, which, by regulating local calcium influx, may provide the basis for spike-timing dependent plasticity anticipated by theory.

| S-EPMC7837495 | biostudies-literature

Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models.

Project description:Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including genetic predispositions to AF development. Genome-wide association studies have identified a number of genetic variants in association with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research is insufficient for understanding the functional impacts of PITX2 variants on AF. Linking PITX2 properties to ion channels, cells, tissues, atriums and the whole heart, computational models provide a supplementary tool for achieving a quantitative understanding of the functional role of PITX2 in remodelling atrial structure and function to predispose to AF. It is hoped that computational approaches incorporating all we know about PITX2-related structural and electrical remodelling would provide better understanding into its proarrhythmic effects leading to development of improved anti-AF therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.

| S-EPMC8307824 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data