Thalamic oscillations modulate membrane properties of cat thalamic reticular neurons
Bibliographic record
Abstract
Spindle oscillation is generated in intrathalamic circuits and thalamic reticular (RE) neurons act as pacemakers. To assess the consequences of this oscillation on membrane properties of RE neurons, we performed in vivo intracellular recordings in anesthetized cats during periods of intense synaptic activity, represented by spindle waves, compared to silent periods (interspindle lulls) that corresponded to a hyperpolarization of about −75 mV. During spindles, the massive activation of T-currents generated powerful low-threshold spikes (LTSs) that changed the membrane potential of RE cells by 10–20 mV and increased their membrane fluctuations by one order of magnitude (from 1–3 mV to 10 mV). Besides, the generation of LTSs decreased the apparent input resistance by ∼80% for ∼20–30 mseconds, in a cyclic way. The changes in basic membrane properties proved to be functionally significant for RE neurons, because both synaptic and intrinsic responsiveness were enhanced during active network states. Thus, synaptic responses tested by stimulating afferent (corticothalamic and thalamo-RE) pathways displayed increased spiking. The same result was found for intrinsic responses using positive and negative current pulses, and sinusoidal wave currents. Bursting discharges, responsible for the generation of the above changes, proved to be a very robust and reliable firing pattern in RE neurons. Our data demonstrate that ongoing network activity of RE neurons during spindle oscillations influence the expression of intrinsic properties of these pacemaking neurons, similarly to the previously shown effect exerted by spontaneously occurring synaptic activity on the intrinsic properties of thalamocortical and neocortical 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".