Determinants of rebound burst responses in rat cerebellar nuclear neurons to physiological stimuli
Bibliographic record
Abstract
KEY POINTS: Cerebellar Purkinje cells project GABAergic inhibitory input to neurons of the deep cerebellar nuclei (DCN) that generate a rebound increase in firing, but the specific patterns of input that might elicit a rebound response have not been established. We used recordings of Purkinje cell firing obtained during perioral whisker stimulation in vivo to create a physiological stimulus template to activate Purkinje cell afferents in vitro. DCN cell bursts were evoked by the stimulus pattern but not in relation to the perioral whisker stimulus, complex spikes or regular patterns within the Purkinje cell record. Reverse correlation revealed that bursts were triggered by an elevation-pause pattern of Purkinje cell firing, with pause duration a key factor in burst generation. Our data identify for the first time a physiological pattern of Purkinje cell input that can be encoded by the generation of rebound bursts in DCN cells. ABSTRACT: The end result of signal processing in cerebellar cortex is encoded in the output of Purkinje cells that project inhibitory input to deep cerebellar nuclear (DCN) neurons. DCN cells can respond to a period of inhibition in vitro with a rebound burst of firing, yet the optimal physiological pattern of Purkinje cell input that might evoke a rebound burst is unknown. The current study used spike trains recorded from rat Purkinje cells in response to perioral stimuli in vivo to create a physiological pattern to stimulate Purkinje cell axons in vitro. The perioral stimulus-evoked Purkinje cell firing pattern proved to be virtually ineffective in evoking a rebound burst despite the ability to reliably evoke rebounds using a traditional brief 100 Hz stimulus. Similarly, neither complex spike firing nor Purkinje cell patterns identified by CV2 analysis were reliably associated with rebound bursts. Reverse correlation revealed that the optimal Purkinje cell input to evoke a rebound burst was a sequential increase in mean firing rate of at least 30 Hz above baseline over 250 ms followed by a reduction of 40-60 Hz below baseline for up to 500 ms. The most important factor was the duration of a pause in Purkinje cell firing that allowed DCN cells to recover from a state of net inhibitory influence. These data indicate that physiological patterns of Purkinje cell firing can elicit rebound bursts in DCN cells in vitro, with pauses in Purkinje cell firing rate acting as a key stimulus for DCN cell rebound responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".