Modulation of synaptic transmission in neocortex by network activities
Bibliographic record
Abstract
Neocortical neurons integrate inputs from thousands of presynaptic neurons that fire in vivo with frequencies that can reach 20 Hz. An important issue in understanding cortical integration is to determine the actual impact of presynaptic firing on postsynaptic neuron in the context of an active network. We used dual intracellular recordings from synaptically connected neurons or microstimulation to study the properties of spontaneous and evoked single-axon excitatory postsynaptic potentials (EPSPs) in vivo, in barbiturate or ketamine-xylazine anaesthetized cats. We found that active states of the cortical network were associated with higher variability and decrease in amplitude and duration of the EPSPs owing to a shunting effect. Moreover, the number of apparent failures markedly increased during active states as compared with silent states. Single-axon EPSPs in vivo showed mainly paired-pulse facilitation, and the paired-pulse ratio increased during active states as compare to silent states, suggesting a decrease in release probability during active states. Raising extracellular Ca(2+) concentration to 2.5-3.0 mm by reverse microdialysis reduced the number of apparent failures and significantly increased the mean amplitude of individual synaptic potentials. Quantitative analysis of spontaneous synaptic activity suggested that the proportion of presynaptic activity that impact at the soma of a cortical neuron in vivo was low because of a high failure rate, a shunting effect and probably dendritic filtering. We conclude that during active states of cortical network, the efficacy of synaptic transmission in individual synapses is low, thus safe transmission of information requires synchronized activity of a large population of presynaptic neurons.
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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) | 0.000 | 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 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".