Literature DB >> 25448430

The neural circuitry and molecular mechanisms underlying delay and trace eyeblink conditioning in mice.

Yi Yang1, Chen Lei2, Hua Feng3, Jian-feng Sui4.   

Abstract

Classical eyeblink conditioning (EBC), a simple form of associative learning, has long been served as a model for motor learning and modulation. The neural circuitry of EBC has been studied in detail in rabbits. However, its underlying molecular mechanisms remain unclear. The advent of mouse transgenics has generated new perspectives on the studies of the neural substrates and molecular mechanisms essential for EBC. Results about EBC in mice differ in some aspects from those obtained in other mammals. Here, we review the current studies about the neural circuitry and molecular mechanisms underlying delay and trace EBC in mice. We conclude that brainstem-cerebellar circuit plays an essential role in DEC while the amygdala modulates this process, and that the medial prefrontal cortex (mPFC) as a candidate is involved in the extra-cerebellar mechanism underlying delay eyeblink conditioning (DEC) in mice. We propose the Amygdala-Cerebellum-Prefrontal Cortex-Dynamic-Conditioning Model (ACPDC model) for DEC in mice. As to trace eyeblink conditioning (TEC), the forebrain regions may play an essential role in it, whereas cerebellar cortex seems to be out of the neural circuitry in mice. Moreover, the molecular mechanisms underlying DEC and TEC in mice differ from each other. This review provides some new information and perspectives for further research on EBC.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  DEC; Eyeblink conditioning; Mice; Molecular mechanisms; Neural circuitry; TEC

Mesh:

Year:  2014        PMID: 25448430     DOI: 10.1016/j.bbr.2014.10.006

Source DB:  PubMed          Journal:  Behav Brain Res        ISSN: 0166-4328            Impact factor:   3.332


  13 in total

Review 1.  Regulation and Interaction of Multiple Types of Synaptic Plasticity in a Purkinje Neuron and Their Contribution to Motor Learning.

Authors:  Tomoo Hirano
Journal:  Cerebellum       Date:  2018-12       Impact factor: 3.847

2.  Cerebellar Processing Common to Delay and Trace Eyelid Conditioning.

Authors:  Hunter E Halverson; Andrei Khilkevich; Michael D Mauk
Journal:  J Neurosci       Date:  2018-07-16       Impact factor: 6.167

Review 3.  Conserved regulators of cognitive aging: From worms to humans.

Authors:  Rachel N Arey; Coleen T Murphy
Journal:  Behav Brain Res       Date:  2016-06-18       Impact factor: 3.332

4.  Inactivation of the interpositus nucleus during unpaired extinction does not prevent extinction of conditioned eyeblink responses or conditioning-specific reflex modification.

Authors:  Lauren B Burhans; Bernard G Schreurs
Journal:  Behav Neurosci       Date:  2019-03-14       Impact factor: 1.912

Review 5.  Impact of anesthesia exposure in early development on learning and sensory functions.

Authors:  Daniil P Aksenov; Michael J Miller; Conor J Dixon; Alexander Drobyshevsky
Journal:  Dev Psychobiol       Date:  2020-03-01       Impact factor: 3.038

6.  Cerebellar-dependent associative learning is impaired in very preterm born children and young adults.

Authors:  Liliane Tran; Britta M Huening; Olaf Kaiser; Bernd Schweiger; Selma Sirin; Harald H Quick; Ursula Felderhoff-Mueser; Dagmar Timmann
Journal:  Sci Rep       Date:  2017-12-21       Impact factor: 4.379

7.  Compromised Survival of Cerebellar Molecular Layer Interneurons Lacking GDNF Receptors GFRα1 or RET Impairs Normal Cerebellar Motor Learning.

Authors:  Maria Christina Sergaki; Juan Carlos López-Ramos; Stefanos Stagkourakis; Agnès Gruart; Christian Broberger; José María Delgado-García; Carlos F Ibáñez
Journal:  Cell Rep       Date:  2017-06-06       Impact factor: 9.423

8.  A method for combining multiple-units readout of optogenetic control with natural stimulation-evoked eyeblink conditioning in freely-moving mice.

Authors:  Jie Zhang; Kai-Yuan Zhang; Li-Bin Zhang; Wei-Wei Zhang; Hua Feng; Zhong-Xiang Yao; Bo Hu; Hao Chen
Journal:  Sci Rep       Date:  2019-02-12       Impact factor: 4.379

9.  Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice.

Authors:  Shogo Ohmae; Javier F Medina
Journal:  Nat Neurosci       Date:  2015-11-09       Impact factor: 24.884

10.  A model for time interval learning in the Purkinje cell.

Authors:  Daniel Majoral; Ajmal Zemmar; Raul Vicente
Journal:  PLoS Comput Biol       Date:  2020-02-10       Impact factor: 4.475

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