Switching between “On” and “Off” states of persistent activity in lateral entorhinal layer III neurons
Bibliographic record
Abstract
Persistent neural spiking maintains information during a working memory task when a stimulus is no longer present. During retention, this activity needs to be stable to distractors. More importantly, when retention is no longer relevant, cessation of the activity is necessary to enable processing and retention of subsequent information. Here, by means of intracellular recording with sharp microelectrode in in vitro rat brain slices, we demonstrate that single principal layer III neurons of the lateral entorhinal cortex (EC) generate persistent spiking activity with a novel ability to reliably toggle between spiking activity and a silent state. Our data indicates that in the presence of muscarinic receptor activation, persistent activity following an excitatory input may be induced and that a subsequent excitatory input can terminate this activity and cause the neuron to return to a silent state. Moreover, application of inhibitory hyperpolarizing stimuli is neither able to decrease the frequency of the persistent activity nor terminate it. The persistent activity can also be initiated and terminated by synchronized synaptic stimuli of layer II/III of the perirhinal cortex. The neuronal ability to switch "On" and "Off" persistent activity may facilitate the concurrent representation of temporally segregated information arriving in the EC and being directed toward the hippocampus.
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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".