Literature DB >> 15242668

Detection of sequences in the cerebellar cortex: numerical estimate of the possible number of tidal-wave inducing sequences represented.

Fahad Sultan1, Detlef Heck.   

Abstract

The two major cortices of the brain--the cerebral and cerebellar cortex--are massively connected through intercalated nuclei (pontine, cerebellar and thalamic nuclei). We suggest that the two cortices co-operate by generating precise temporal patterns in the cerebral cortex that are detected in the cerebellar cortex as temporal patterns assembled spatially in the mossy fibers. We will begin by showing that the tidal-wave mechanism works in the cerebellar cortex as a read-out mechanism for such spatio-temporal patterns due to the synchronous activity they generate in the parallel fiber system which drives the Purkinje cells--the output neurons of the cerebellar cortex--to fire action potentials. We will review the anatomy of the mossy fibers and show that within a "beam", or "row" of cerebellar cortex the mossy fibers in principle could embed a vast number of tidal-wave generating sequences. Based on anatomical data we will argue that the cerebellar mossy fiber-granule cell-Purkinje cell system can potentially detect and--through learning--select from an enormous number of spatio-temporal patterns.

Mesh:

Year:  2003        PMID: 15242668     DOI: 10.1016/j.jphysparis.2004.01.016

Source DB:  PubMed          Journal:  J Physiol Paris        ISSN: 0928-4257


  9 in total

1.  The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex.

Authors:  Mike Gilbert
Journal:  Cerebellum       Date:  2021-12-13       Impact factor: 3.847

2.  A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties.

Authors:  Sergio Solinas; Thierry Nieus; Egidio D'Angelo
Journal:  Front Cell Neurosci       Date:  2010-05-14       Impact factor: 5.505

3.  Understanding Cerebellum Granular Layer Network Computations through Mathematical Reconstructions of Evoked Local Field Potentials.

Authors:  Harilal Parasuram; Bipin Nair; Giovanni Naldi; Egidio D'Angelo; Shyam Diwakar
Journal:  Ann Neurosci       Date:  2017-10-26

4.  Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control.

Authors:  Shyam Diwakar; Paola Lombardo; Sergio Solinas; Giovanni Naldi; Egidio D'Angelo
Journal:  PLoS One       Date:  2011-07-19       Impact factor: 3.240

5.  How and Why the Cerebellum Recodes Input Signals: An Alternative to Machine Learning.

Authors:  Mike Gilbert; R Chris Miall
Journal:  Neuroscientist       Date:  2021-02-09       Impact factor: 7.235

6.  Exploring the significance of morphological diversity for cerebellar granule cell excitability.

Authors:  Catriona M Houston; Efthymia Diamanti; Maria Diamantaki; Elena Kutsarova; Anna Cook; Fahad Sultan; Stephen G Brickley
Journal:  Sci Rep       Date:  2017-04-13       Impact factor: 4.379

Review 7.  The cerebellar Golgi cell and spatiotemporal organization of granular layer activity.

Authors:  Egidio D'Angelo; Sergio Solinas; Jonathan Mapelli; Daniela Gandolfi; Lisa Mapelli; Francesca Prestori
Journal:  Front Neural Circuits       Date:  2013-05-17       Impact factor: 3.492

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

Authors:  Anna Cattani; Sergio Solinas; Claudio Canuto
Journal:  Front Comput Neurosci       Date:  2016-04-19       Impact factor: 2.380

Review 9.  Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue.

Authors:  Egidio D'Angelo; Alberto Antonietti; Stefano Casali; Claudia Casellato; Jesus A Garrido; Niceto Rafael Luque; Lisa Mapelli; Stefano Masoli; Alessandra Pedrocchi; Francesca Prestori; Martina Francesca Rizza; Eduardo Ros
Journal:  Front Cell Neurosci       Date:  2016-07-08       Impact factor: 5.505

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.