Since its discovery, long-term synaptic plasticity has been of great interest to neuroscientists as a therapeutic target for brain disorders, especially disorders affecting learning and memory. Scientific and technological advances have provided an array of tools for enhancing synaptic plasticity. In some cases, experimental manipulations that augment plasticity have succeeded in augmenting learning (Tang et al., 1999; van Praag et al., 1999; Lee and Silva, 2009). However, in many cases, manipulations that augment plasticity have impaired learning (Migaud et al., 1998; Uetani et al., 2000; Gu et al., 2002; Cox et al., 2003; Rutten et al., 2008; Navakkode et al., 2022). Surprisingly, there have been few attempts to reconcile these conflicting findings with a mechanistic explanation for why enhancing synaptic plasticity can have opposite effects on learning. Such mechanistic insight about how enhanced synaptic plasticity functions in vivo could facilitate the development of this approach as a viable clinical intervention for learning disorders, recovery from stroke or brain injury, dementia, addiction, and other neurological and psychiatric disorders.

Recently, we proposed a testable hypothesis about what can go wrong with augmented plasticity in vivo, based on experimental and theoretical analysis of learning in mice with enhanced associative synaptic plasticity in the cerebellum (Nguyen-Vu et al., 2017). Associative LTD at the cerebellar PF-Purkinje cell synapses (PF-Purkinje cell LTD) has been implicated in certain cerebellum-dependent learning tasks and not others, based in part on the observation of selective learning impairments in mouse lines with impaired PF-Purkinje cell LTD (reviewed in Raymond and Medina, 2018; De Zeeuw et al., 2021). Initially, we expected that mice with enhanced PF-Purkinje cell LTD would exhibit the exact opposite behavioral phenotype as mice with impaired PF-Purkinje LTD, that is, enhancement of learning on the same behavioral tasks in which mice with impaired PF-Purkinje cell LTD exhibit impaired learning. Contrary to this expectation, double knockout of the major histocompatibility class I molecules MHCI H2-K^b and H2-D^b (MHCI K^bD^b−/−), which enhances PF-Purkinje cell LTD (McConnell et al., 2009), results in the very same, specific oculomotor learning impairment as observed in mice with impaired PF-Purkinje cell LTD (Nguyen-Vu et al., 2017). To explain the puzzling observation that the enhancement of a plasticity mechanism could yield the same behavioral phenotype as its impairment, we hypothesized that enhanced LTD prevents learning by allowing spontaneous activity in the circuit to aberrantly recruit and saturate this form of plasticity, making it unavailable at the specific synapses where it is needed to support learning. Two key predictions of this hypothesis were confirmed experimentally by previous work: optogenetic stimulation of the circuit designed to induce PF-Purkinje cell LTD before training recapitulated in WT mice the same, specific oculomotor learning deficit observed in the MHCI K^bD^b−/− mice with enhanced LTD; and a behavioral manipulation designed to reverse PF-Purkinje cell LTD before oculomotor training reversed the learning deficit in MHCI K^bD^b−/− mice (Nguyen-Vu et al., 2017).

Here, we further tested the hypothesis that the enhancement of PF-Purkinje cell LTD can result in its aberrant recruitment by ongoing neural activity and consequent increased threshold for its induction and reduced availability to support learning. First, we replicated key behavioral findings in a different line of mice with enhanced PF-Purkinje cell LTD. Purkinje cell-specific knock out of the Fragile X gene Fmr1 enhances PF-Purkinje cell LTD (Koekkoek et al., 2005). We show that these L7-Fmr1 KO mice are selectively impaired on LTD-dependent oculomotor learning tasks, and that their learning deficit can be reversed with behavioral pre-training designed to reverse PF-Purkinje cell LTD, as previously reported in the MHCI K^bD^b−/− mice with enhanced PF-Purkinje cell LTD. We then test a new prediction about a pharmacological treatment to reverse the learning deficit in mice with enhanced associative synaptic plasticity. The experimental results support and extend the influential concept of a history-dependent sliding threshold for synaptic plasticity (Bienenstock et al., 1982) as a regulator of learning.

A question of central scientific and clinical importance is why the enhancement of synaptic plasticity can impair rather than enhance learning. One hypothesis is that a lower threshold for the induction of plasticity might cause it to be over-recruited during training, at synapses that should not have undergone plasticity in addition to synapses where it would support adaptive behavioral changes, thereby corrupting the memory trace (Migaud et al., 1998; Koekkoek et al., 2005). An alternative hypothesis is that the enhancement of plasticity might allow spontaneous activity in the circuit to aberrantly recruit the plasticity mechanism even before training begins, and thereby reduce its availability during training to support new learning (Figure 6). Nguyen-Vu et al., 2017 described this reduced availability as saturation of plasticity, but more generally, the reduced availability could reflect an increased threshold for the induction plasticity (Bienenstock et al., 1982; Leet et al., 2022). This threshold metaplasticity hypothesis differs from the over-recruitment hypothesis by suggesting that the enhanced plasticity mechanism is under- rather than over- recruited during training. It also differs by suggesting that the problem with enhanced plasticity arises because of what it does to the circuit before training, rather than how it functions during training, and therefore more readily accounts for effects of pre-training manipulations on learning in mice with enhanced plasticity. The current findings thus bolster the evidence from Nguyen-Vu et al., 2017 suggesting threshold metaplasticity rather than over-recruitment as the cause of impaired learning in mice with enhanced associative LTD at the cerebellar parallel fiber-Purkinje cell synapses.

Figure 6

Download asset Open asset

We found that L7-Fmr1 KO mice with enhanced PF-Purkinje cell LTD exhibit impaired rather than enhanced learning on oculomotor learning tasks in which PF-Purkinje cell LTD has been implicated, as previously shown for MHCI K^bD^b−/− mice. In both mouse lines, behavioral manipulations designed to prevent or reverse the induction of PF-Purkinje cell LTD during the period before training reversed the learning impairment. The additional finding that pharmacological suppression of neural activity with diazepam can enhance subsequent learning in mice with enhanced associative LTD provides new evidence that these mice are not incapable of LTD-dependent learning. Rather, the interaction of the ongoing neural activity in the circuit with the enhanced plasticity appears to create a state in which LTD is unavailable to support learning. However, this state of reduced availability can be reversed when the patterns of neural activity that create it are eliminated. The capacity for PF-Purkinje cell LTD-dependent learning is dynamically regulated by the recent history of activity.

Comparison across closely related cerebellum-dependent learning tasks reveals the specific behavioral consequences of enhanced PF-Purkinje cell LTD. Although it was the main candidate mechanism of cerebellum-dependent learning for many decades, there is growing evidence that PF-Purkinje cell LTD contributes selectively to certain cerebellum-dependent learning tasks, and not others (Shibuki et al., 1996; Boyden et al., 2006; De Zeeuw et al., 2021). Oculomotor learning is particularly advantageous for analyzing the function of PF-Purkinje cell LTD during learning because this plasticity mechanism is thought to contribute differentially to a set of closely related oculomotor learning tasks that all depend on the same vestibular, visual and motor signaling pathways through the cerebellar flocculus. Despite the shared cerebellar and extra-cerebellar circuitry, a number of experimental approaches, including ex vivo slice physiology (Inoshita and Hirano, 2018; Jang et al., 2020; Shim et al., 2022), optogenetic stimulation (Kimpo et al., 2014; Nguyen-Vu et al., 2017; Zhang et al., 2023) and studies of oculomotor learning in mice with impaired LTD (Boyden et al., 2006; Hansel et al., 2006; Kakegawa et al., 2018) have suggested a selective contribution of PF-Purkinje cell LTD to OKR adaptation and VOR-increase learning induced by high-frequency (≥1 Hz) vestibular and visual stimuli, with less or no contribution to VOR-decrease learning or VOR-increase learning induced with lower frequency vestibular and visual stimuli.

In both the L7-Fmr1 KO and MHCI K^bD^b−/− mice with enhanced PF-Purkinje cell LTD, the learning impairments and the enhancement of learning by behavioral or pharmacological pre-training manipulations were remarkably selective for the oculomotor learning tasks in which PF-Purkinje cell LTD has been most strongly implicated. Both lines of mice were profoundly impaired on high-frequency VOR-increase learning and OKR adaptation. The effects of behavioral and pharmacological pre-training were strikingly specific for the same oculomotor learning tasks. Moreover, pre-training only enhanced learning in the L7-Fmr1 KO and MHCI K^bD^b−/− mice, and not WT mice, consistent with the pre-training selectively reversing limitations caused by enhanced LTD, rather than generally enhancing cerebellum-dependent learning. In the L7-Fmr1 KO mice, low-frequency as well as high-frequency VOR-increase learning was impaired. However, the behavioral and diazepam pre-treatments designed to prevent or reverse LTD during the period before training only improved the high-frequency and not the low-frequency VOR-increase learning in the L7-Fmr1 KO mice, suggesting different mechanistic underpinnings of the low- and high-frequency impairments. In other words, the deletion of Fmr1 from Purkinje cells may have two distinct effects: enhancement of PF-Purkinje cell LTD, which recapitulates the high-frequency VOR-increase learning phenotypes observed in the MHCI K^bD^b−/− mice with enhanced LTD, plus disruption of an additional cellular mechanism that contributes to low-frequency VOR-increase learning. Overall, in the two lines of mice with enhanced PF-Purkinje cell LTD, both the learning impairments and the effects of manipulations designed to reverse or prevent LTD before training were remarkably selective for the specific oculomotor learning tasks with the strongest evidence for a contribution of PF-Purkinje cell LTD. This oculomotor learning task specificity strengthens the evidence that the behavioral phenotypes in the L7-Fmr1 KO and MHCI K^bD^b−/− mice are a reflection of the enhanced LTD at PF-Purkinje cell synapses rather than other functional properties of the Purkinje cells, their synapses, or other cells or plasticity mechanisms within the same circuit.

The very similar behavioral phenotypes observed when PF-Purkinje cell LTD is enhanced by manipulating different molecular cascades further strengthens the evidence that their shared effect of enhancing LTD is responsible for their shared learning impairments, rather than other, off-target effects of the molecular manipulations. MHCI H2- D^b acts on MAP kinase and integrin via interaction with immune receptors such as PirB (Shatz, 2009), whereas Fmr1 acts by inhibiting mGluR-dependent dendritic protein translation (Huber et al., 2002). Cell-type-specific manipulations localize the site where loss of expression of MHCI H2-D^b or Fmr1 yields the oculomotor learning deficits to the Purkinje cells. Each yields enhancement of PF-Purkinje cell LTD, as measured by the ability of protocols that fail to induce LTD in slices from WT mice to induce LTD in slices from MHCI K^bD^b−/− (McConnell et al., 2009) and L7-Fmr1 KO mice (Koekkoek et al., 2005). Along with this shared effect of enhancing PF-Purkinje cell LTD, loss of MHCI H2-D^b or Fmr1 expression from the Purkinje cells may each have additional effects on the intrinsic and synaptic properties of Purkinje cells, which are likely to be different for the two molecules. These latter, ‘off-target’ effects may contribute to the impairment of L7-Fmr1 KO mice on low-frequency VOR-increase learning (Figure 4), which was not observed in MHCI K^bD^b−/− mice (Figure 1—figure supplement 1, Nguyen-Vu et al., 2017). Off-target effects also might have contributed to the different cerebellar learning phenotypes reported previously in the two lines of mice – enhanced rotorod learning in the MHCI K^bD^b−/− mice (McConnell et al., 2009), but impaired eyeblink conditioning in the L7-Fmr1 KO mice, global Fmr1 KO mice and Fragile X patients (Koekkoek et al., 2005). However, the behavioral tasks used in those previous studies were also different, and our current results highlight the importance of the specific choice of behavioral task for assessing cerebellar learning, and the differential dependence of different cerebellar learning tasks on specific molecular and cellular processes within the cerebellum.

The current findings and the metaplasticity conceptual framework could guide the development of new clinical approaches for Fragile X syndrome and a range of other neurological and psychiatric conditions with enhanced associative plasticity. Pre-treatment with the FDA-approved drug diazepam restored the capacity for high-frequency VOR-increase learning and OKR adaptation in the L7-Fmr1 KO mice without compromising other forms of oculomotor learning or baseline oculomotor performance. This was true even for VOR-decrease learning, which may depend on LTP of the same population of PF-Purkinje cell synapses that undergo LTD during VOR-increase learning (Shim et al., 2022). In other words, diazepam pre-treatment fully rescued the learning impairments with no apparent side effects on other, closely related functions of the same neural circuitry. This specificity enhances its therapeutic potential. At the same time, this approach of suppressing neural activity during a pre-training period may be generally applicable to any learning task, motor or cognitive (Rochefort et al., 2013; Badura et al., 2018; Ashburn et al., 2020; Frontera et al., 2020; Stoodley and Tsai, 2021; Hwang et al., 2022), that is impaired by enhanced associative LTD. Pharmacological suppression of neural activity should suppress PF-Purkinje cell LTD throughout the cerebellum, and hence may have the general effect of restoring all regions of the cerebellum to a state compatible with new LTD-dependent learning. This generality of the pharmacological approach stands in contrast to the behavioral pre-training approach, which would require extensive additional knowledge and experimentation to design the appropriate behavioral pre-treatment to reset each functional region of the cerebellum to a state compatible with LTD-dependent learning, and different behavioral pre-treatments would be required to target each of the many functional regions supporting the myriad motor and cognitive functions of the cerebellum. Thus, from a practical standpoint, pharmacological pre-treatment to prevent or reverse the recruitment of LTD before training and thereby lower the threshold for its subsequent recruitment during training is a more feasible and general approach to restoring the capacity for PF-Purkinje cell LTD-dependent learning. Such an approach could be tested even when the specific plasticity mechanism responsible for a learning deficit has not been identified, because lower activity should reduce induction of most associative plasticity mechanisms. Thus, the approach of limiting neural activity during a period before training to reset elevated thresholds for plasticity may be broadly applicable throughout the brain for resetting neural circuits to a state compatible with adaptive plasticity and new learning. Consistent with this, suppression of neural activity in the retina has been successfully employed to reset the visual circuitry and enable recovery from amblyopia in adult mice and cats (Fong et al., 2021). The suppression of neural activity may be an especially useful approach if plasticity is pathologically enhanced in areas like the cerebellum or basal ganglia, with a high level of spontaneous spiking activity.

The results predict history-dependent changes in the availability of PF-Purkinje cell LTD to support learning due to activity-dependent changes in the threshold for LTD. Threshold metaplasticity has not been directly documented at these synapses, however several factors that influence climbing fiber-induced calcium influx or the probability of LTD induction have been identified (reviewed in Zang and De Schutter, 2019), which could provide the mechanistic substrate for threshold metaplasticity. These include plasticity of the climbing fiber synapse onto the Purkinje cell (Hansel and Linden, 2000), plasticity of Purkinje cell dendritic excitability (Ohtsuki et al., 2012), changes in the number of spikes in a climbing fiber burst (Mathy et al., 2009; Medina and Lisberger, 2008), changes in short-term plasticity mechanisms at the PF-Purkinje cell synapses (Hunley et al., 2023), and plasticity of inhibitory synapses onto the Purkinje cells (Kano et al., 1992; Kawaguchi and Hirano, 2007; Rowan et al., 2018).

The concept of experience-dependent changes in the threshold for synaptic plasticity has been highly influential in theoretical and computational neuroscience (Abraham and Bear, 1996; Benusková et al., 1999; Cooper and Bear, 2012; Hulme et al., 2012; Lee, 2022), since the foundational work of Bienenstock et al., 1982. However, experimental evidence for whether and how such threshold metaplasticity supports the function of neural circuits has been limited, and derived largely from studies of the effects of sensory deprivation on the functional connectivity of circuits (Mioche and Singer, 1989; Kirkwood et al., 1996; Philpot et al., 2003; He et al., 2007; Yee et al., 2017) rather than studies of learning per se. Analysis of threshold metaplasticity in the context of cerebellum-dependent learning and associative LTD offers a new perspective on the Bienenstock, Cooper,and Munro (BCM) model (Bienenstock et al., 1982). Most fundamentally, the present results predict threshold metaplasticity at synapses where the plasticity is not Hebbian. A sliding threshold for plasticity has been conceived as a mechanism for countering an instability inherent in Hebbian LTP whereby correlated pre- and post-synaptic activity strengthens a synapse, which leads to an increase in correlated activity, which in turn leads to further strengthening. An increased threshold for LTP in response to an increase in neural activity would counter this instability and provide a mechanism to stabilize firing rates and synaptic weights within a desired range (van Rossum et al., 2000; Toyoizumi et al., 2014; Yger and Gilson, 2015; Zenke et al., 2017). In contrast, plasticity at the cerebellar PF-Purkinje cell synapse is described as ‘anti-Hebbian’ because the associative form of plasticity is LTD. Associative LTD lacks the instability inherent in Hebbian LTP. Moreover, an increased threshold for LTD in response to an increase in neural activity or a decreased threshold for LTD in response to decreased neural activity would tend to oppose rather than support the stability of firing rates of the postsynaptic Purkinje cells. Yet, the present results provide evidence for these activity-dependent changes in the threshold for cerebellar LTD. Thus, rather than supporting homeostatic control of firing rates, the central function of threshold metaplasticity at these synapses may be to limit the amount of plasticity. In addition, the finding that manipulations of neural activity during the pre-training period had different effects in the mice with enhanced LTD than in WT mice suggests that changes in the threshold for plasticity may be driven, not directly by firing rates, but by the recent history of activity-dependent induction of plasticity (Montgomery and Madison, 2002; Lev-Ram et al., 2003; Hunley et al., 2023; Abraham, 2008; Martin and Kosik, 2002; Redondo and Morris, 2011) A plasticity-driven increase in the threshold for further plasticity could serve to protect newly acquired memories from being overwritten (Fusi et al., 2005; Benna and Fusi, 2016). A second potential function would be to separate memories acquired in close succession onto the synapses of different Purkinje cells, in contrast to findings in the amygdala, where there is evidence that a plasticity-driven decrease in the threshold for further plasticity supports the allocation of memories acquired in close succession to the same neurons (Han et al., 2007; Benna and Fusi, 2016; Cai et al., 2016; Rashid et al., 2016; Lau et al., 2020).

All experimental procedures were approved by the Administrative Panel on Laboratory Animal Care (APLAC protocol # 9143) at Stanford University.

All code used for data acquisition (https://github.com/RaymondLab/Code/tree/Master/Experiment%20Protocols) and analysis (https://github.com/RaymondLab/Code/tree/Master/Tools/VOR_Analysis) is available at https://github.com/RaymondLab/Code (copy archived at RaymondLab, 2024). Underlying data for each figure is available in the corresponding Excel file.

1. 1. Abraham WC
   2. Bear MF

   (1996) Metaplasticity: the plasticity of synaptic plasticity

   Trends in Neurosciences 19:126–130.

   https://doi.org/10.1016/s0166-2236(96)80018-x

   • PubMed
   • Google Scholar
2. 1. Abraham WC

   (2008) Metaplasticity: tuning synapses and networks for plasticity

   Nature Reviews. Neuroscience 9:387.

   https://doi.org/10.1038/nrn2356

   • PubMed
   • Google Scholar
3. 1. Ashburn SM
   2. Flowers DL
   3. Napoliello EM
   4. Eden GF

   (2020) Cerebellar function in children with and without dyslexia during single word processing

   Human Brain Mapping 41:120–138.

   https://doi.org/10.1002/hbm.24792

   • PubMed
   • Google Scholar
4. 1. Badura A
   2. Verpeut JL
   3. Metzger JW
   4. Pereira TD
   5. Pisano TJ
   6. Deverett B
   7. Bakshinskaya DE
   8. Wang SSH

   (2018) Normal cognitive and social development require posterior cerebellar activity

   eLife 7:e36401.

   https://doi.org/10.7554/eLife.36401

   • PubMed
   • Google Scholar
5. 1. Barmack NH
   2. Pettorossi VE

   (1980) Influence of a GABA agonist, diazepam, on the vestibuloocular reflexes of the rabbit

   Brain Research Bulletin 5:705–712.

   https://doi.org/10.1016/0361-9230(80)90116-1

   • Google Scholar
6. 1. Benna MK
   2. Fusi S

   (2016) Computational principles of synaptic memory consolidation

   Nature Neuroscience 19:1697–1706.

   https://doi.org/10.1038/nn.4401

   • PubMed
   • Google Scholar
7. 1. Benusková L
   2. Ebner FF
   3. Diamond ME
   4. Armstrong-James M

   (1999)

   Computational study of experience-dependent plasticity in adult rat cortical barrel-column

   Network 10:303–323.

   • PubMed
   • Google Scholar
8. 1. Bienenstock EL
   2. Cooper LN
   3. Munro PW

   (1982) Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex

   The Journal of Neuroscience 2:32–48.

   https://doi.org/10.1523/JNEUROSCI.02-01-00032.1982

   • PubMed
   • Google Scholar
9. 1. Boyden ES
   2. Raymond JL

   (2003) Active reversal of motor memories reveals rules governing memory encoding

   Neuron 39:1031–1042.

   https://doi.org/10.1016/s0896-6273(03)00562-2

   • PubMed
   • Google Scholar
10. 1. Boyden ES
    2. Katoh A
    3. Raymond JL

    (2004) Cerebellum-dependent learning: the role of multiple plasticity mechanisms

    Annual Review of Neuroscience 27:581–609.

    https://doi.org/10.1146/annurev.neuro.27.070203.144238

    • PubMed
    • Google Scholar
11. 1. Boyden ES
    2. Katoh A
    3. Pyle JL
    4. Chatila TA
    5. Tsien RW
    6. Raymond JL

    (2006) Selective engagement of plasticity mechanisms for motor memory storage

    Neuron 51:823–834.

    https://doi.org/10.1016/j.neuron.2006.08.026

    • PubMed
    • Google Scholar
12. 1. Broussard DM
    2. Kassardjian CD

    (2004) Learning in a simple motor system

    Learning & Memory 11:127–136.

    https://doi.org/10.1101/lm.65804

    • Google Scholar
13. 1. Cai DJ
    2. Aharoni D
    3. Shuman T
    4. Shobe J
    5. Biane J
    6. Song W
    7. Wei B
    8. Veshkini M
    9. La-Vu M
    10. Lou J
    11. Flores SE
    12. Kim I
    13. Sano Y
    14. Zhou M
    15. Baumgaertel K
    16. Lavi A
    17. Kamata M
    18. Tuszynski M
    19. Mayford M
    20. Golshani P
    21. Silva AJ

    (2016) A shared neural ensemble links distinct contextual memories encoded close in time

    Nature 534:115–118.

    https://doi.org/10.1038/nature17955

    • PubMed
    • Google Scholar
14. 1. Cooper LN
    2. Bear MF

    (2012) The BCM theory of synapse modification at 30: interaction of theory with experiment

    Nature Reviews. Neuroscience 13:798–810.

    https://doi.org/10.1038/nrn3353

    • PubMed
    • Google Scholar
15. 1. Cox PR
    2. Fowler V
    3. Xu B
    4. Sweatt JD
    5. Paylor R
    6. Zoghbi HY

    (2003) Mice lacking Tropomodulin-2 show enhanced long-term potentiation, hyperactivity, and deficits in learning and memory

    Molecular and Cellular Neurosciences 23:1–12.

    https://doi.org/10.1016/s1044-7431(03)00025-3

    • PubMed
    • Google Scholar
16. Book

    1. Cullen KE

    (2023) Vestibular motor control

    In: Cullen KE, editors. Handbook of Clinical Neurology. Elsevier. pp. 31–54.

    https://doi.org/10.1016/B978-0-323-98818-6.00022-4

    • Google Scholar
17. 1. De Zeeuw CI
    2. Lisberger SG
    3. Raymond JL

    (2021) Diversity and dynamism in the cerebellum

    Nature Neuroscience 24:160–167.

    https://doi.org/10.1038/s41593-020-00754-9

    • PubMed
    • Google Scholar
18. 1. Fong M-F
    2. Duffy KR
    3. Leet MP
    4. Candler CT
    5. Bear MF

    (2021) Correction of amblyopia in cats and mice after the critical period

    eLife 10:e70023.

    https://doi.org/10.7554/eLife.70023

    • PubMed
    • Google Scholar
19. 1. Frontera JL
    2. Baba Aissa H
    3. Sala RW
    4. Mailhes-Hamon C
    5. Georgescu IA
    6. Léna C
    7. Popa D

    (2020) Bidirectional control of fear memories by cerebellar neurons projecting to the ventrolateral periaqueductal grey

    Nature Communications 11:5207.

    https://doi.org/10.1038/s41467-020-18953-0

    • PubMed
    • Google Scholar
20. 1. Fusi S
    2. Drew PJ
    3. Abbott LF

    (2005) Cascade models of synaptically stored memories

    Neuron 45:599–611.

    https://doi.org/10.1016/j.neuron.2005.02.001

    • PubMed
    • Google Scholar
21. 1. Gittis AH
    2. du Lac S

    (2006) Intrinsic and synaptic plasticity in the vestibular system

    Current Opinion in Neurobiology 16:385–390.

    https://doi.org/10.1016/j.conb.2006.06.012

    • PubMed
    • Google Scholar
22. 1. Gonshor A
    2. Jones GM

    (1973)

    Proceedings: Changes of human vestibulo-ocular response induced by vision-reversal during head rotation

    The Journal of Physiology 234:102P–103P.

    • PubMed
    • Google Scholar
23. 1. Gu Y
    2. McIlwain KL
    3. Weeber EJ
    4. Yamagata T
    5. Xu B
    6. Antalffy BA
    7. Reyes C
    8. Yuva-Paylor L
    9. Armstrong D
    10. Zoghbi H
    11. Sweatt JD
    12. Paylor R
    13. Nelson DL

    (2002) Impaired conditioned fear and enhanced long-term potentiation in Fmr2 knock-out mice

    The Journal of Neuroscience 22:2753–2763.

    https://doi.org/10.1523/JNEUROSCI.22-07-02753.2002

    • PubMed
    • Google Scholar
24. 1. Guo CC
    2. Ke MC
    3. Raymond JL

    (2014) Cerebellar encoding of multiple candidate error cues in the service of motor learning

    The Journal of Neuroscience 34:9880–9890.

    https://doi.org/10.1523/JNEUROSCI.5114-13.2014

    • PubMed
    • Google Scholar
25. 1. Han JH
    2. Kushner SA
    3. Yiu AP
    4. Cole CJ
    5. Matynia A
    6. Brown RA
    7. Neve RL
    8. Guzowski JF
    9. Silva AJ
    10. Josselyn SA

    (2007) Neuronal competition and selection during memory formation

    Science 316:457–460.

    https://doi.org/10.1126/science.1139438

    • Google Scholar
26. 1. Hansel C
    2. Linden DJ

    (2000) Long-term depression of the cerebellar climbing fiber--Purkinje neuron synapse

    Neuron 26:473–482.

    https://doi.org/10.1016/s0896-6273(00)81179-4

    • PubMed
    • Google Scholar
27. 1. Hansel C
    2. de Jeu M
    3. Belmeguenai A
    4. Houtman SH
    5. Buitendijk GHS
    6. Andreev D
    7. De Zeeuw CI
    8. Elgersma Y

    (2006) alphaCaMKII Is essential for cerebellar LTD and motor learning

    Neuron 51:835–843.

    https://doi.org/10.1016/j.neuron.2006.08.013

    • PubMed
    • Google Scholar
28. 1. He HY
    2. Ray B
    3. Dennis K
    4. Quinlan EM

    (2007) Experience-dependent recovery of vision following chronic deprivation amblyopia

    Nature Neuroscience 10:1134–1136.

    https://doi.org/10.1038/nn1965

    • PubMed
    • Google Scholar
29. 1. Huber KM
    2. Gallagher SM
    3. Warren ST
    4. Bear MF

    (2002) Altered synaptic plasticity in a mouse model of fragile X mental retardation

    PNAS 99:7746–7750.

    https://doi.org/10.1073/pnas.122205699

    • PubMed
    • Google Scholar
30. 1. Hulme SR
    2. Jones OD
    3. Ireland DR
    4. Abraham WC

    (2012) Calcium-dependent but action potential-independent BCM-like metaplasticity in the hippocampus

    The Journal of Neuroscience 32:6785–6794.

    https://doi.org/10.1523/JNEUROSCI.0634-12.2012

    • PubMed
    • Google Scholar
31. Conference

    1. Hunley C
    2. Mitra S
    3. Pugh JR

    (2023)

    Parallel fiber metaplasticity mediated by cannabinoid type 1 receptors

    Program No. 213.05. 2023 Neuroscience Meeting Planner.

    • Google Scholar
32. 1. Hwang KD
    2. Kim SJ
    3. Lee YS

    (2022) Cerebellar circuits for classical fear conditioning

    Frontiers in Cellular Neuroscience 16:836948.

    https://doi.org/10.3389/fncel.2022.836948

    • PubMed
    • Google Scholar
33. 1. Inoshita T
    2. Hirano T

    (2018) Occurrence of long-term depression in the cerebellar flocculus during adaptation of optokinetic response

    eLife 7:e36209.

    https://doi.org/10.7554/eLife.36209

    • PubMed
    • Google Scholar
34. 1. Ito M
    2. Shiida T
    3. Yagi N
    4. Yamamoto M

    (1974a) The cerebellar modification of rabbit’s horizontal vestibulo-ocular reflex induced by sustained head rotation combined with visual stimulation

    Proceedings of the Japan Academy 50:85–89.

    https://doi.org/10.2183/pjab1945.50.85

    • Google Scholar
35. 1. Ito M
    2. Shiida T
    3. Yagi N
    4. Yamamoto M

    (1974b) Visual influence on rabbit horizontal vestibulo-ocular reflex presumably effected via the cerebellar flocculus

    Brain Research 65:170–174.

    https://doi.org/10.1016/0006-8993(74)90344-8

    • PubMed
    • Google Scholar
36. 1. Ito M

    (1982) Cerebellar control of the vestibulo-ocular reflex--around the flocculus hypothesis

    Annual Review of Neuroscience 5:275–296.

    https://doi.org/10.1146/annurev.ne.05.030182.001423

    • PubMed
    • Google Scholar
37. 1. Ito M
    2. Kano M

    (1982) Long-lasting depression of parallel fiber-Purkinje cell transmission induced by conjunctive stimulation of parallel fibers and climbing fibers in the cerebellar cortex

    Neuroscience Letters 33:253–258.

    https://doi.org/10.1016/0304-3940(82)90380-9

    • PubMed
    • Google Scholar
38. 1. Jang DC
    2. Shim HG
    3. Kim SJ

    (2020) Intrinsic plasticity of cerebellar purkinje cells contributes to motor memory consolidation

    The Journal of Neuroscience 40:4145–4157.

    https://doi.org/10.1523/JNEUROSCI.1651-19.2020

    • PubMed
    • Google Scholar
39. 1. Kakegawa W
    2. Katoh A
    3. Narumi S
    4. Miura E
    5. Motohashi J
    6. Takahashi A
    7. Kohda K
    8. Fukazawa Y
    9. Yuzaki M
    10. Matsuda S

    (2018) Optogenetic control of synaptic AMPA receptor endocytosis reveals roles of LTD in motor learning

    Neuron 99:985–998.

    https://doi.org/10.1016/j.neuron.2018.07.034

    • PubMed
    • Google Scholar
40. 1. Kano M
    2. Rexhausen U
    3. Dreessen J
    4. Konnerth A

    (1992) Synaptic excitation produces a long-lasting rebound potentiation of inhibitory synaptic signals in cerebellar Purkinje cells

    Nature 356:601–604.

    https://doi.org/10.1038/356601a0

    • PubMed
    • Google Scholar
41. 1. Kawaguchi S
    2. Hirano T

    (2007) Sustained structural change of GABA(A) receptor-associated protein underlies long-term potentiation at inhibitory synapses on A cerebellar Purkinje neuron

    The Journal of Neuroscience 27:6788–6799.

    https://doi.org/10.1523/JNEUROSCI.1981-07.2007

    • PubMed
    • Google Scholar
42. 1. Kimpo RR
    2. Rinaldi JM
    3. Kim CK
    4. Payne HL
    5. Raymond JL

    (2014) Gating of neural error signals during motor learning

    eLife 3:e02076.

    https://doi.org/10.7554/eLife.02076

    • PubMed
    • Google Scholar
43. 1. Kirkwood A
    2. Rioult MC
    3. Bear MF

    (1996) Experience-dependent modification of synaptic plasticity in visual cortex

    Nature 381:526–528.

    https://doi.org/10.1038/381526a0

    • PubMed
    • Google Scholar
44. 1. Koekkoek S
    2. V. Alphen A
    3. V.d. Burg J
    4. Grosveld F
    5. Galjart N
    6. De Zeeuw C

    (1997) Gain adaptation and phase dynamics of compensatory eye movements in mice

    Genes and Function 1:175–190.

    https://doi.org/10.1046/j.1365-4624.1997.00018.x

    • Google Scholar
45. 1. Koekkoek SKE
    2. Yamaguchi K
    3. Milojkovic BA
    4. Dortland BR
    5. Ruigrok TJH
    6. Maex R
    7. De Graaf W
    8. Smit AE
    9. VanderWerf F
    10. Bakker CE
    11. Willemsen R
    12. Ikeda T
    13. Kakizawa S
    14. Onodera K
    15. Nelson DL
    16. Mientjes E
    17. Joosten M
    18. De Schutter E
    19. Oostra BA
    20. Ito M
    21. De Zeeuw CI

    (2005) Deletion of FMR1 in Purkinje cells enhances parallel fiber LTD, enlarges spines, and attenuates cerebellar eyelid conditioning in Fragile X syndrome

    Neuron 47:339–352.

    https://doi.org/10.1016/j.neuron.2005.07.005

    • PubMed
    • Google Scholar
46. 1. Lau JMH
    2. Rashid AJ
    3. Jacob AD
    4. Frankland PW
    5. Schacter DL
    6. Josselyn SA

    (2020) The role of neuronal excitability, allocation to an engram and memory linking in the behavioral generation of a false memory in mice

    Neurobiology of Learning and Memory 174:107284.

    https://doi.org/10.1016/j.nlm.2020.107284

    • PubMed
    • Google Scholar
47. 1. Lee YS
    2. Silva AJ

    (2009) The molecular and cellular biology of enhanced cognition

    Nature Reviews. Neuroscience 10:126–140.

    https://doi.org/10.1038/nrn2572

    • PubMed
    • Google Scholar
48. 1. Lee HK

    (2022) Metaplasticity framework for cross-modal synaptic plasticity in adults

    Frontiers in Synaptic Neuroscience 14:1087042.

    https://doi.org/10.3389/fnsyn.2022.1087042

    • PubMed
    • Google Scholar
49. 1. Leet MP
    2. Bear MF
    3. Gaier ED

    (2022) Metaplasticity: a key to visual recovery from amblyopia in adulthood?

    Current Opinion in Ophthalmology 33:512–518.

    https://doi.org/10.1097/ICU.0000000000000901

    • PubMed
    • Google Scholar
50. 1. Lev-Ram V
    2. Mehta SB
    3. Kleinfeld D
    4. Tsien RY

    (2003) Reversing cerebellar long-term depression

    PNAS 100:15989–15993.

    https://doi.org/10.1073/pnas.2636935100

    • PubMed
    • Google Scholar
51. 1. Li J
    2. Smith SS
    3. McElligott JG

    (1995) Cerebellar nitric oxide is necessary for vestibulo-ocular reflex adaptation, a sensorimotor model of learning

    Journal of Neurophysiology 74:489–494.

    https://doi.org/10.1152/jn.1995.74.1.489

    • PubMed
    • Google Scholar
52. 1. Linden DJ
    2. Connor JA

    (1995) Long-term synaptic depression

    Annual Review of Neuroscience 18:319–357.

    https://doi.org/10.1146/annurev.ne.18.030195.001535

    • PubMed
    • Google Scholar
53. 1. Lisberger SG
    2. Miles FA
    3. Zee DS

    (1984) Signals used to compute errors in monkey vestibuloocular reflex: possible role of flocculus

    Journal of Neurophysiology 52:1140–1153.

    https://doi.org/10.1152/jn.1984.52.6.1140

    • PubMed
    • Google Scholar
54. 1. Martin KC
    2. Kosik KS

    (2002) Synaptic tagging -- who’s it?

    Nature Reviews. Neuroscience 3:813–820.

    https://doi.org/10.1038/nrn942

    • PubMed
    • Google Scholar
55. 1. Mathy A
    2. Ho SSN
    3. Davie JT
    4. Duguid IC
    5. Clark BA
    6. Häusser M

    (2009) Encoding of oscillations by axonal bursts in inferior olive neurons

    Neuron 62:388–399.

    https://doi.org/10.1016/j.neuron.2009.03.023

    • PubMed
    • Google Scholar
56. 1. McConnell MJ
    2. Huang YH
    3. Datwani A
    4. Shatz CJ

    (2009) H2-K(b) and H2-D(b) regulate cerebellar long-term depression and limit motor learning

    PNAS 106:6784–6789.

    https://doi.org/10.1073/pnas.0902018106

    • PubMed
    • Google Scholar
57. 1. McElligott JG
    2. Beeton P
    3. Polk J

    (1998) Effect of cerebellar inactivation by lidocaine microdialysis on the vestibuloocular reflex in goldfish

    Journal of Neurophysiology 79:1286–1294.

    https://doi.org/10.1152/jn.1998.79.3.1286

    • PubMed
    • Google Scholar
58. 1. Medina JF
    2. Lisberger SG

    (2008) Links from complex spikes to local plasticity and motor learning in the cerebellum of awake-behaving monkeys

    Nature Neuroscience 11:1185–1192.

    https://doi.org/10.1038/nn.2197

    • PubMed
    • Google Scholar
59. 1. Michnovicz JJ
    2. Bennett MVL

    (1987) Effects of rapid cerebellectomy on adaptive gain control of the vestibulo-ocular reflex in alert goldfish

    Experimental Brain Research 66:287–294.

    https://doi.org/10.1007/BF00243305

    • PubMed
    • Google Scholar
60. 1. Mientjes EJ
    2. Nieuwenhuizen I
    3. Kirkpatrick L
    4. Zu T
    5. Hoogeveen-Westerveld M
    6. Severijnen L
    7. Rifé M
    8. Willemsen R
    9. Nelson DL
    10. Oostra BA

    (2006) The generation of a conditional Fmr1 knock out mouse model to study Fmrp function in vivo

    Neurobiology of Disease 21:549–555.

    https://doi.org/10.1016/j.nbd.2005.08.019

    • PubMed
    • Google Scholar
61. 1. Migaud M
    2. Charlesworth P
    3. Dempster M
    4. Webster LC
    5. Watabe AM
    6. Makhinson M
    7. He Y
    8. Ramsay MF
    9. Morris RG
    10. Morrison JH
    11. O’Dell TJ
    12. Grant SG

    (1998) Enhanced long-term potentiation and impaired learning in mice with mutant postsynaptic density-95 protein

    Nature 396:433–439.

    https://doi.org/10.1038/24790

    • PubMed
    • Google Scholar
62. 1. Miles FA
    2. Fuller JH

    (1974) Adaptive plasticity in the vestibulo-ocular responses of the rhesus monkey

    Brain Research 80:512–516.

    https://doi.org/10.1016/0006-8993(74)91035-x

    • PubMed
    • Google Scholar
63. 1. Mioche L
    2. Singer W

    (1989) Chronic recordings from single sites of kitten striate cortex during experience-dependent modifications of receptive-field properties

    Journal of Neurophysiology 62:185–197.

    https://doi.org/10.1152/jn.1989.62.1.185

    • PubMed
    • Google Scholar
64. 1. Montgomery JM
    2. Madison DV

    (2002) State-dependent heterogeneity in synaptic depression between pyramidal cell pairs

    Neuron 33:765–777.

    https://doi.org/10.1016/s0896-6273(02)00606-2

    • PubMed
    • Google Scholar
65. 1. Nagao S

    (1983) Effects of vestibulocerebellar lesions upon dynamic characteristics and adaptation of vestibulo-ocular and optokinetic responses in pigmented rabbits

    Experimental Brain Research 53:36–46.

    https://doi.org/10.1007/BF00239396

    • PubMed
    • Google Scholar
66. 1. Navakkode S
    2. Zhai J
    3. Wong YP
    4. Li G
    5. Soong TW

    (2022) Enhanced long-term potentiation and impaired learning in mice lacking alternative exon 33 of Ca_V1.2 calcium channel

    Translational Psychiatry 12:1.

    https://doi.org/10.1038/s41398-021-01683-2

    • PubMed
    • Google Scholar
67. 1. Nguyen-Vu TB
    2. Zhao GQ
    3. Lahiri S
    4. Kimpo RR
    5. Lee H
    6. Ganguli S
    7. Shatz CJ
    8. Raymond JL

    (2017) A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity

    eLife 6:e20147.

    https://doi.org/10.7554/eLife.20147

    • PubMed
    • Google Scholar
68. 1. Ohtsuki G
    2. Piochon C
    3. Adelman JP
    4. Hansel C

    (2012) SK2 channel modulation contributes to compartment-specific dendritic plasticity in cerebellar purkinje cells

    Neuron 75:108–120.

    https://doi.org/10.1016/j.neuron.2012.05.025

    • Google Scholar
69. 1. Pastor AM
    2. de la Cruz RR
    3. Baker R

    (1994) Cerebellar role in adaptation of the goldfish vestibuloocular reflex

    Journal of Neurophysiology 72:1383–1394.

    https://doi.org/10.1152/jn.1994.72.3.1383

    • Google Scholar
70. 1. Payne HL
    2. Raymond JL

    (2017) Magnetic eye tracking in mice

    eLife 6:e29222.

    https://doi.org/10.7554/eLife.29222

    • PubMed
    • Google Scholar
71. 1. Philpot BD
    2. Espinosa JS
    3. Bear MF

    (2003) Evidence for altered NMDA receptor function as a basis for metaplasticity in visual cortex

    The Journal of Neuroscience 23:5583–5588.

    https://doi.org/10.1523/JNEUROSCI.23-13-05583.2003

    • PubMed
    • Google Scholar
72. 1. Rashid AJ
    2. Yan C
    3. Mercaldo V
    4. Hsiang H-LL
    5. Park S
    6. Cole CJ
    7. De Cristofaro A
    8. Yu J
    9. Ramakrishnan C
    10. Lee SY
    11. Deisseroth K
    12. Frankland PW
    13. Josselyn SA

    (2016) Competition between engrams influences fear memory formation and recall

    Science 353:383–387.

    https://doi.org/10.1126/science.aaf0594

    • PubMed
    • Google Scholar
73. 1. Raymond JL
    2. Medina JF

    (2018) Computational principles of supervised learning in the cerebellum

    Annual Review of Neuroscience 41:233–253.

    https://doi.org/10.1146/annurev-neuro-080317-061948

    • PubMed
    • Google Scholar
74. Software

    1. RaymondLab

    (2024) Code, version swh:1:rev:3375250d17929d524a242098a2b195a31bbcdab6

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:ef16b8af990e78a9a76b0cb83b5cd3b92e7530e4;origin=https://github.com/RaymondLab/Code;visit=swh:1:snp:5000b5dcf61aa2cac987bbe6af9204d011668dda;anchor=swh:1:rev:3375250d17929d524a242098a2b195a31bbcdab6
75. 1. Redondo RL
    2. Morris RGM

    (2011) Making memories last: the synaptic tagging and capture hypothesis

    Nature Reviews Neuroscience 12:17–30.

    https://doi.org/10.1038/nrn2963

    • Google Scholar
76. 1. Riss J
    2. Cloyd J
    3. Gates J
    4. Collins S

    (2008) Benzodiazepines in epilepsy: pharmacology and pharmacokinetics

    Acta Neurologica Scandinavica 118:69–86.

    https://doi.org/10.1111/j.1600-0404.2008.01004.x

    • PubMed
    • Google Scholar
77. 1. Robinson DA

    (1976) Adaptive gain control of vestibuloocular reflex by the cerebellum

    Journal of Neurophysiology 39:954–969.

    https://doi.org/10.1152/jn.1976.39.5.954

    • PubMed
    • Google Scholar
78. 1. Rochefort C
    2. Lefort JM
    3. Rondi-Reig L

    (2013) The cerebellum: a new key structure in the navigation system

    Frontiers in Neural Circuits 7:35.

    https://doi.org/10.3389/fncir.2013.00035

    • PubMed
    • Google Scholar
79. 1. Rowan MJM
    2. Bonnan A
    3. Zhang K
    4. Amat SB
    5. Kikuchi C
    6. Taniguchi H
    7. Augustine GJ
    8. Christie JM

    (2018) Graded control of climbing-fiber-mediated plasticity and learning by inhibition in the cerebellum

    Neuron 99:999–1015.

    https://doi.org/10.1016/j.neuron.2018.07.024

    • PubMed
    • Google Scholar
80. 1. Rutten K
    2. Misner DL
    3. Works M
    4. Blokland A
    5. Novak TJ
    6. Santarelli L
    7. Wallace TL

    (2008) Enhanced long-term potentiation and impaired learning in phosphodiesterase 4D-knockout (PDE4D) mice

    The European Journal of Neuroscience 28:625–632.

    https://doi.org/10.1111/j.1460-9568.2008.06349.x

    • PubMed
    • Google Scholar
81. 1. Ryu JH
    2. McCabe BF

    (1974)

    effects of diazepam and dimenhydrinate on the resting activity of the vestibular neuron

    Aerospace Medicine 45:1177–1179.

    • PubMed
    • Google Scholar
82. 1. Shatz CJ

    (2009) MHC class I: an unexpected role in neuronal plasticity

    Neuron 64:40–45.

    https://doi.org/10.1016/j.neuron.2009.09.044

    • PubMed
    • Google Scholar
83. 1. Shibuki K
    2. Gomi H
    3. Chen L
    4. Bao S
    5. Kim JJ
    6. Wakatsuki H
    7. Fujisaki T
    8. Fujimoto K
    9. Katoh A
    10. Ikeda T
    11. Chen C
    12. Thompson RF
    13. Itohara S

    (1996) Deficient cerebellar long-term depression, impaired eyeblink conditioning, and normal motor coordination in GFAP mutant mice

    Neuron 16:587–599.

    https://doi.org/10.1016/s0896-6273(00)80078-1

    • PubMed
    • Google Scholar
84. Conference

    1. Shim H
    2. Fanning A
    3. Raymond J

    (2022)

    Plasticity mechanisms for bidirectional oculomotor learning

    Program No. 213.05. 2022 Neuroscience Meeting Planner.

    • Google Scholar
85. 1. Stoodley CJ
    2. Tsai PT

    (2021) Adaptive prediction for social contexts: The cerebellar contribution to typical and atypical social behaviors

    Annual Review of Neuroscience 44:475–493.

    https://doi.org/10.1146/annurev-neuro-100120-092143

    • PubMed
    • Google Scholar
86. 1. Takeuchi T
    2. Ohtsuki G
    3. Yoshida T
    4. Fukaya M
    5. Wainai T
    6. Yamashita M
    7. Yamazaki Y
    8. Mori H
    9. Sakimura K
    10. Kawamoto S
    11. Watanabe M
    12. Hirano T
    13. Mishina M

    (2008) Enhancement of both long-term depression induction and optokinetic response adaptation in mice lacking delphilin

    PLOS ONE 3:e2297.

    https://doi.org/10.1371/journal.pone.0002297

    • PubMed
    • Google Scholar
87. 1. Tang YP
    2. Shimizu E
    3. Dube GR
    4. Rampon C
    5. Kerchner GA
    6. Zhuo M
    7. Liu G
    8. Tsien JZ

    (1999) Genetic enhancement of learning and memory in mice

    Nature 401:63–69.

    https://doi.org/10.1038/43432

    • PubMed
    • Google Scholar
88. 1. Toyoizumi T
    2. Kaneko M
    3. Stryker MP
    4. Miller KD

    (2014) Modeling the dynamic interaction of Hebbian and homeostatic plasticity

    Neuron 84:497–510.

    https://doi.org/10.1016/j.neuron.2014.09.036

    • PubMed
    • Google Scholar
89. 1. Uetani N
    2. Kato K
    3. Ogura H
    4. Mizuno K
    5. Kawano K
    6. Mikoshiba K
    7. Yakura H
    8. Asano M
    9. Iwakura Y

    (2000) Impaired learning with enhanced hippocampal long-term potentiation in PTPdelta-deficient mice

    The EMBO Journal 19:2775–2785.

    https://doi.org/10.1093/emboj/19.12.2775

    • PubMed
    • Google Scholar
90. 1. van Praag H
    2. Christie BR
    3. Sejnowski TJ
    4. Gage FH

    (1999) Running enhances neurogenesis, learning, and long-term potentiation in mice

    PNAS 96:13427–13431.

    https://doi.org/10.1073/pnas.96.23.13427

    • PubMed
    • Google Scholar
91. 1. van Rossum MCW
    2. Bi GQ
    3. Turrigiano GG

    (2000) Stable Hebbian learning from spike timing-dependent plasticity

    The Journal of Neuroscience 20:8812–8821.

    https://doi.org/10.1523/JNEUROSCI.20-23-08812.2000

    • PubMed
    • Google Scholar
92. 1. Wang W
    2. Nakadate K
    3. Masugi-Tokita M
    4. Shutoh F
    5. Aziz W
    6. Tarusawa E
    7. Lorincz A
    8. Molnár E
    9. Kesaf S
    10. Li YQ
    11. Fukazawa Y
    12. Nagao S
    13. Shigemoto R

    (2014) Distinct cerebellar engrams in short-term and long-term motor learning

    PNAS 111:E188–E193.

    https://doi.org/10.1073/pnas.1315541111

    • PubMed
    • Google Scholar
93. 1. Yee AX
    2. Hsu YT
    3. Chen L

    (2017) A metaplasticity view of the interaction between homeostatic and Hebbian plasticity

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 372:20160155.

    https://doi.org/10.1098/rstb.2016.0155

    • PubMed
    • Google Scholar
94. 1. Yger P
    2. Gilson M

    (2015) Models of metaplasticity: A review of concepts

    Frontiers in Computational Neuroscience 9:138.

    https://doi.org/10.3389/fncom.2015.00138

    • PubMed
    • Google Scholar
95. 1. Zang Y
    2. De Schutter E

    (2019) Climbing fibers provide graded error signals in cerebellar learning

    Frontiers in Systems Neuroscience 13:46.

    https://doi.org/10.3389/fnsys.2019.00046

    • PubMed
    • Google Scholar
96. 1. Zenke F
    2. Gerstner W
    3. Ganguli S

    (2017) The temporal paradox of Hebbian learning and homeostatic plasticity

    Current Opinion in Neurobiology 43:166–176.

    https://doi.org/10.1016/j.conb.2017.03.015

    • PubMed
    • Google Scholar
97. 1. Zhang XM
    2. Ng AHL
    3. Tanner JA
    4. Wu WT
    5. Copeland NG
    6. Jenkins NA
    7. Huang JD

    (2004) Highly restricted expression of cre recombinase in cerebellar purkinje cells

    Genesis 40:45–51.

    https://doi.org/10.1002/gene.20062

    • PubMed
    • Google Scholar
98. Preprint

    1. Zhang K
    2. Yang Z
    3. Gaffield MA
    4. Gross GG
    5. Arnold DB
    6. Christie JM

    (2023) Molecular Layer Disinhibition Unlocks Climbing-Fiber-Instructed Motor Learning in the Cerebellum

    bioRxiv.

    https://doi.org/10.1101/2023.08.04.552059

    • Google Scholar

Author details

1. Amin MD Shakhawat

   Department of Neurobiology, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   amins@stanford.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3776-3699

2. Jacqueline G Foltz

   Department of Neurobiology, Stanford University, Stanford, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

3. Adam B Nance

   Department of Neurobiology, Stanford University, Stanford, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Jaydev Bhateja

   Department of Neurobiology, Stanford University, Stanford, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Jennifer L Raymond

   Department of Neurobiology, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   jenr@stanford.edu

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-8145-747X

• 400

  views

• 39

  downloads

• 1

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.