Selective Amplification of Neocortical Neuronal Output by Fast Prepotentials InVivo
Bibliographic record
Abstract
Neocortical cells integrate inputs from thousands of presynaptic neurons distributed along their dendritic arbors. Propagation of postsynaptic potentials to the soma is crucial in determining neuronal output. Using intracellular recordings in anesthetized and non-anesthetized, naturally awake and sleeping cats, we found evidence for generation of fast, all-or-none events recorded at the soma in about 20% of regular-spiking and intrinsically-bursting neurons. These events, termed fast prepotentials (FPPs), were suppressed by hyperpolarizing the neurons or by inhibiting synaptic transmission with perfusion of Ca2+-free artificial cerebrospinal fluid. FPPs could be evoked by activation of specific cortical inputs and allowed neurons to fire at more hyperpolarized levels of membrane potentials. Thus, FPPs represent a powerful mechanism to boost the output of neocortical neurons in response to given inputs. We further found evidence for modulation of FPPs generation across the waking-sleep cycle, indicating important changes in the integrative properties of neocortical neurons in different states of vigilance. We suggest that FPPs represent attenuated spikes generated in hot spots of the dendritic arbor and constitute a powerful mechanism to reinforce the functional connections between specific elements of the cortical networks.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".