Literature DB >> 17517494

The cerebellum as a liquid state machine.

Tadashi Yamazaki1, Shigeru Tanaka.   

Abstract

We examined closely the cerebellar circuit model that we have proposed previously. The model granular layer generates a finite but very long sequence of active neuron populations without recurrence, which is able to represent the passage of time. For all the possible binary patterns fed into mossy fibres, the circuit generates the same number of different sequences of active neuron populations. Model Purkinje cells that receive parallel fiber inputs from neurons in the granular layer learn to stop eliciting spikes at the timing instructed by the arrival of signals from the inferior olive. These functional roles of the granular layer and Purkinje cells are regarded as a liquid state generator and readout neurons, respectively. Thus, the cerebellum that has been considered to date as a biological counterpart of a perceptron is reinterpreted to be a liquid state machine that possesses powerful information processing capability more than a perceptron.

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Year:  2007        PMID: 17517494     DOI: 10.1016/j.neunet.2007.04.004

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  34 in total

Review 1.  Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning.

Authors:  Egidio D'Angelo; Lisa Mapelli; Claudia Casellato; Jesus A Garrido; Niceto Luque; Jessica Monaco; Francesca Prestori; Alessandra Pedrocchi; Eduardo Ros
Journal:  Cerebellum       Date:  2016-04       Impact factor: 3.847

2.  Computational Architecture of the Granular Layer of Cerebellum-Like Structures.

Authors:  Peter Bratby; James Sneyd; John Montgomery
Journal:  Cerebellum       Date:  2017-02       Impact factor: 3.847

Review 3.  Computational models of timing mechanisms in the cerebellar granular layer.

Authors:  Tadashi Yamazaki; Shigeru Tanaka
Journal:  Cerebellum       Date:  2009-06-05       Impact factor: 3.847

4.  A model for complex sequence learning and reproduction in neural populations.

Authors:  Sergio Oscar Verduzco-Flores; Mark Bodner; Bard Ermentrout
Journal:  J Comput Neurosci       Date:  2011-09-02       Impact factor: 1.621

5.  Sequential Pattern Formation in the Cerebellar Granular Layer.

Authors:  Peter Bratby; James Sneyd; John Montgomery
Journal:  Cerebellum       Date:  2017-04       Impact factor: 3.847

6.  Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks.

Authors:  Philippe Vincent-Lamarre; Guillaume Lajoie; Jean-Philippe Thivierge
Journal:  J Comput Neurosci       Date:  2016-09-02       Impact factor: 1.621

7.  Nonlinear modeling of causal interrelationships in neuronal ensembles.

Authors:  Theodoros P Zanos; Spiros H Courellis; Theodore W Berger; Robert E Hampson; Sam A Deadwyler; Vasilis Z Marmarelis
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2008-08       Impact factor: 3.802

Review 8.  Re-evaluating Circuit Mechanisms Underlying Pattern Separation.

Authors:  N Alex Cayco-Gajic; R Angus Silver
Journal:  Neuron       Date:  2019-02-20       Impact factor: 17.173

9.  Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study.

Authors:  Jesús A Garrido; Eduardo Ros; Egidio D'Angelo
Journal:  Front Comput Neurosci       Date:  2013-05-22       Impact factor: 2.380

10.  Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation.

Authors:  Jesús A Garrido; Niceto R Luque; Egidio D'Angelo; Eduardo Ros
Journal:  Front Neural Circuits       Date:  2013-10-09       Impact factor: 3.492

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