Climbing-fiber induced state transitions in cerebellar Purkinje cells are controlled by synaptic conductance changes
Bibliographic record
Abstract
Purkinje cells have been previously modeled as a systemundergoing a saddle-node bifurcation of fixed pointsfrom rest to firing and a saddle homoclinic orbit bifur-cation from firing to rest. In vitro, this dynamical struc-ture is a result of the neuron’s intrinsic membraneproperties and is associated with bistability within a lim-ited range of low firing frequencies, where a unidirec-tional climbing fiber (CF) input is able to toggle the cellbetween a firing (“up”)andrest(“down”)state.Weidentified several factors that contribute to bistabilityand the ability for a unidirectional input (CF) to togglecell output, including a slow K+ current activated duringspike discharge or followingsynaptic depolarizations.However, input conditions that determine the probabil-ity for a Purkinje cell to express bistabilityin vivo havenot been determined. A key difference in vivo is the pre-sence of tonic background input to the dendrites fromparallel fiber (PF) and stellate cell inputs. We tested thehypothesis that dendritic inputs control the dynamics ofPurkinje cell firing, and can thus regulate the ability forCFs to induce toggling of Purkinje cell output.Presentation of mixed excitatory and inhibitory dendri-tic current noise (I-noise) or conductance noise (g-noise)to a two-compartment 5-equation model of the Purkinjeneuron had differing effects on spike output. Mixed I-noise increased the probability of observing CF-evokedtransitions to a down state whether the model was in thelow frequency-bistable regime or not. The size and timecourse of the currents associated with different statetransitions suggested that properly timed PF and/or stel-late cell inputs could affect the ability for CFs to invokePurkinje cell transitions. However, conductance noiseprevented any CF-evoked transitions and the model washighly sensitive to the E:I ratio. Spike trains with physio-logical mean frequencies and high coefficient of variation(CV) were also found. Spike triggered averages duringg-noise revealed that the spikes were being driven bysynaptic inputs and not intrinsic dynamics, indicating ashift in computational properties between high and lowconductance states.ConclusionsHere we show that bistabilityinaPurkinjeneuroncanbe controlled by the amount of synaptic input itreceives. Of the two types of noise we used, I-noisecould cause spontaneous state transitions, but g-noisecould not, suggesting that CF-associated toggling wouldnot occur in high conductance states. These resultscould explain the discrepancy between in vivo and in vitrorecordings regarding CF-induced state transitions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".