Interneuron NMDA receptors change the gear of motor learning in the cerebellar machine
Bibliographic record
Abstract
Since the 19th century the cerebellum has been known for its function in motor control including the maintenance of equilibrium (balance, posture, eye movement), coordination of the timing and force of muscle groups, adjustment of muscle tone, learning motor skills and speech. Supported by the rich connections between the cerebellum, the cerebral cortex and the limbic system, the cerebellum is increasingly recognized for its non-motor functions such as cognition (language and social interaction), motivation and emotions and consequently for its role in autism-spectrum, obsessive–compulsive, attention-deficit hyperactivity and bipolar disorders, and schizophrenia (Schmahmann et al. 2007; Yang et al. 2018). The need for more detailed cellular and molecular analyses of the cerebellar circuitry and plasticity is pressing. As per the adaptive-filter model (Dean et al. 2010), the cerebellar cortex is a signal processing device in which the signal components are carried by the parallel fibres (PFs) directly to the Purkinje cells (PCs) as the sole output neurons, or indirectly through the molecular layer interneurons (MLIs), to de-correlate from an error signal delivered by the climbing fibres (CFs). The PF and CF signals are integrated on the PCs and MLIs. Decorrelation of the signal components and the error signal requires the constant adjustment of the relative weights of the PF–PC synapses depending on the presence or absence of the error signal. The cellular correlate of the temporal weight adjustment is the long-term modulation of synaptic strength that is expressed postsynaptically in the PF–PC synapses. Single PF stimulation evokes long-term potentiation (LTP), but the coactivation of the CFs causes long-term depression (LTD) and thus decorrelation from the error (Dean et al. 2010). While LTD and low-frequency-evoked LTP (1 Hz) are known to be essential for motor learning, their mechanism is different: LTD is NMDA receptor (NMDAR) and nitric oxide (NO) dependent, the 1 Hz stimulation-evoked LTP depends only on NO. This implies an important role of NMDARs and NO in vivo in signal integration. Different studies have debated whether NMDARs are activated on the presynaptic membrane of PFs, the axon terminal and somatodendritic region of MLIs or postsynaptically at the adult CF–PC synapses (older than 2 months). Despite detailed analyses of the cerebellar microcircuits, the precise cellular and subcellular localization of NMDARs and their distinct roles in LTD and motor learning have remained controversial. In this issue of The Journal of Physiology, Kono et al. (2019) provide compelling evidence about the role of NMDARs in cerebellar LTD in vitro and motor learning in vivo by generating cell-specific (granule cell (GC), PC, MLI/PC) conditional knock-outs (cKOs) of the obligatory GluN1 NMDAR subunit encoded by the Grin1 gene. They confirmed by patch-clamp recordings in brain slices that functional NMDARs can be deleted from GCs, MLIs and PCs. They tested LTD by paired stimulation protocols and motor learning during optokinetic response adaptation. They found that NMDARs expressed on MLIs are essential to LTD induction and motor learning while NMDARs on GCs and PCs are dispensable (Fig. 1). The acute slice experiments were performed in the presence of GABAA receptor blocker to exclude direct MLI–PC transmission, implying an interaction between the direct and indirect PF pathways. Since the LTD and motor learning are neuronal NO synthase (nNOS) dependent, the authors reasoned that the diffusible messenger NO likely mediated LTD. Indeed, the NO donor 2-(N,N-diethylamino)-diazenolate-2-oxide (DEANO) rescued LTD in MLI/PC cKOs (Fig. 1; note the experimental condition that AMPA receptor exocytosis in the PCs is blocked by botulinum neurotoxin). These results by Kono et al. (2019) represent a major step forward to cut the Gordian knot regarding the precise subcellular localization of NMDARs in the cerebellar microcircuitry and the cellular mechanism of PF–CF signal integration. Future experiments with cell-specific cKO of nNOS will help localize the cellular source of NO. Because optokinetic response adaptation was measured without the GABAA blocker that was employed for LTD in acute slices, manipulation of the activity of cerebellar MLIs specifically expressing excitatory or inhibitory channelrhodopsins in vivo (Kruse et al. 2014) will further establish the distinct roles of interneuron NMDARs in motor and non-motor functions. Exciting results using Cre–LoxP-based cKOs as exemplified by Kono et al. (2019) will prove invaluable in advancing our understanding of how long-term synaptic plasticity contributes to the cerebellar motor and non-motor functions via the engagement of interneurons to adjust the gears in the cerebellar machine (Eccles et al. 1967). No competing interests declared Both authors have read and approved the final version of this manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. L.-Y.W. received funding from the Gouvernement du Canada/Canadian Institutes of Health Research (Institutsde recherche en santé du Canada): PJT – 156439; and Gouvernement du Canada/Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada): Discovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".