Literature DB >> 26112423

Links Between Single-Trial Changes and Learning Rate in Eyelid Conditioning.

Andrei Khilkevich1, Hunter E Halverson1, Jose Ernesto Canton-Josh1, Michael D Mauk2,3.   

Abstract

The discovery of single-trial learning effects, where the presence or absence (or the number) of climbing fiber inputs produces measureable changes in Purkinje cell response and in behavior, represents a major breakthrough in cerebellar learning. Among other things, these observations provide strong links between climbing fiber-mediated plasticity and cerebellar learning. They also demonstrate that cerebellar learning is stochastic, with each instantiation of a movement producing a small increment or decrement in gain. The sum of the small changes give rise to the macroscopic properties of cerebellar learning. We used a relatively large data set from another example of cerebellar-dependent learning, classical conditioning of eyelid responses, to attempt a behavioral replication and extension of single-trial learning effects. As a normal part of training, stimulus-alone trials provide instances where the climbing fiber response would be omitted, similar to non-climbing-fiber trials (gain down) during smooth pursuit training. The consequences of the stimulus-alone trial on the amplitude and timing of the conditioned response on the following paired trials were examined. We find that the amplitude of the conditioned response during the trial after a stimulus-alone trial (no climbing fiber input) was measurably smaller than the amplitude on the previous trials, and this single-trial effect on amplitude is larger for longer interstimulus intervals. The magnitude of the single-trial effect parallels the rate of extinction at different interstimulus intervals supporting the previously observed link between single-trial effects and learning.

Entities:  

Keywords:  Climbing fiber; LTD; LTP; Stochastic learning; Timing

Mesh:

Year:  2016        PMID: 26112423     DOI: 10.1007/s12311-015-0690-8

Source DB:  PubMed          Journal:  Cerebellum        ISSN: 1473-4222            Impact factor:   3.847


  17 in total

1.  A model of Pavlovian eyelid conditioning based on the synaptic organization of the cerebellum.

Authors:  M D Mauk; N H Donegan
Journal:  Learn Mem       Date:  1997 May-Jun       Impact factor: 2.460

2.  Effect of varying the intensity and train frequency of forelimb and cerebellar mossy fiber conditioned stimuli on the latency of conditioned eye-blink responses in decerebrate ferrets.

Authors:  P Svensson; M Ivarsson; G Hesslow
Journal:  Learn Mem       Date:  1997 May-Jun       Impact factor: 2.460

3.  Inhibition of climbing fibres is a signal for the extinction of conditioned eyelid responses.

Authors:  Javier F Medina; William L Nores; Michael D Mauk
Journal:  Nature       Date:  2002-03-21       Impact factor: 49.962

4.  Acquisition and extinction of the classically conditioned eyelid response in the albino rabbit.

Authors:  N SCHNEIDERMAN; I FUENTES; I GORMEZANO
Journal:  Science       Date:  1962-05-18       Impact factor: 47.728

5.  Temporal patterns of inputs to cerebellum necessary and sufficient for trace eyelid conditioning.

Authors:  Brian E Kalmbach; Tatsuya Ohyama; Michael D Mauk
Journal:  J Neurophysiol       Date:  2010-05-19       Impact factor: 2.714

6.  Classical conditioning in rabbits using pontine nucleus stimulation as a conditioned stimulus and inferior olive stimulation as an unconditioned stimulus.

Authors:  J E Steinmetz; D G Lavond; R F Thompson
Journal:  Synapse       Date:  1989       Impact factor: 2.562

7.  Classical conditioning using stimulation of the inferior olive as the unconditioned stimulus.

Authors:  M D Mauk; J E Steinmetz; R F Thompson
Journal:  Proc Natl Acad Sci U S A       Date:  1986-07       Impact factor: 11.205

8.  Neuronal responses of the rabbit cerebellum during acquisition and performance of a classically conditioned nictitating membrane-eyelid response.

Authors:  D A McCormick; R F Thompson
Journal:  J Neurosci       Date:  1984-11       Impact factor: 6.167

9.  Links from complex spikes to local plasticity and motor learning in the cerebellum of awake-behaving monkeys.

Authors:  Javier F Medina; Stephen G Lisberger
Journal:  Nat Neurosci       Date:  2008-09-21       Impact factor: 24.884

10.  Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration.

Authors:  Yan Yang; Stephen G Lisberger
Journal:  Nature       Date:  2014-05-11       Impact factor: 49.962

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  10 in total

1.  Bidirectional short-term plasticity during single-trial learning of cerebellar-driven eyelid movements in mice.

Authors:  Farzaneh Najafi; Javier F Medina
Journal:  Neurobiol Learn Mem       Date:  2019-10-11       Impact factor: 2.877

2.  Movement Rate Is Encoded and Influenced by Widespread, Coherent Activity of Cerebellar Molecular Layer Interneurons.

Authors:  Michael A Gaffield; Jason M Christie
Journal:  J Neurosci       Date:  2017-04-07       Impact factor: 6.167

Review 3.  Depressed by Learning-Heterogeneity of the Plasticity Rules at Parallel Fiber Synapses onto Purkinje Cells.

Authors:  Aparna Suvrathan; Jennifer L Raymond
Journal:  Cerebellum       Date:  2018-12       Impact factor: 3.847

Review 4.  Computational Principles of Supervised Learning in the Cerebellum.

Authors:  Jennifer L Raymond; Javier F Medina
Journal:  Annu Rev Neurosci       Date:  2018-07-08       Impact factor: 12.449

5.  Transitive inference after minimal training in rhesus macaques (Macaca mulatta).

Authors:  Greg Jensen; Fabian Munoz; Anna Meaney; Herbert S Terrace; Vincent P Ferrera
Journal:  J Exp Psychol Anim Learn Cogn       Date:  2021-10       Impact factor: 2.088

6.  Adaptive Acceleration of Visually Evoked Smooth Eye Movements in Mice.

Authors:  Takashi Kodama; Sascha du Lac
Journal:  J Neurosci       Date:  2016-06-22       Impact factor: 6.167

7.  Modulation of Complex-Spike Duration and Probability during Cerebellar Motor Learning in Visually Guided Smooth-Pursuit Eye Movements of Monkeys.

Authors:  Yan Yang; Stephen G Lisberger
Journal:  eNeuro       Date:  2017-07-10

8.  A cerebellar adaptation to uncertain inputs.

Authors:  Andrei Khilkevich; Jose Canton-Josh; Evan DeLord; Michael D Mauk
Journal:  Sci Adv       Date:  2018-05-30       Impact factor: 14.136

9.  Cannabinoids modulate associative cerebellar learning via alterations in behavioral state.

Authors:  Catarina Albergaria; N Tatiana Silva; Dana M Darmohray; Megan R Carey
Journal:  Elife       Date:  2020-10-20       Impact factor: 8.140

10.  Principles of operation of a cerebellar learning circuit.

Authors:  David J Herzfeld; Nathan J Hall; Marios Tringides; Stephen G Lisberger
Journal:  Elife       Date:  2020-04-30       Impact factor: 8.140

  10 in total

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