Nonuniform allocation of hippocampal neurons to place fields across all hippocampal subfields
Bibliographic record
Abstract
The mechanisms governing how the hippocampus selects neurons to exhibit place fields are not well understood. A default assumption in some previous studies was the uniform random draw with replacement (URDWR) model, which, theoretically, maximizes spatial "pattern separation", and predicts a Poisson distribution of the numbers of place fields expressed by a given cell per unit area. The actual distribution of mean firing rates exhibited by a population of hippocampal neurons, however, is approximately exponential or log-normal in a given environment and these rates are somewhat correlated across multiple places, at least under some conditions. The advantage of neural activity-dependent immediate-early gene (IEG) analysis, as a proxy for electrophysiological recording, is the ability to obtain much larger samples of cells, even those whose activity is so sparse that they are overlooked in recording studies. Thus, a more accurate representation of the activation statistics can potentially be achieved. Some previous IEG studies that examined behavior-driven IEG expression in CA1 appear to support URDWR. There was, however, in some of the same studies, an under-recruitment of dentate gyrus granule cells, indicating a highly skewed excitability distribution, which is inconsistent with URDWR. Although it was suggested that this skewness might be related to increased excitability of recently generated granule cells, we show here that CA1, CA3, and subiculum also exhibit cumulative under-recruitment of neurons. Thus, a highly skewed excitability distribution is a general principle common to all major hippocampal subfields. Finally, a more detailed analysis of the frequency distributions of IEG intranuclear transcription foci suggests that a large fraction of hippocampal neurons is virtually silent, even during sleep. Whether the skewing of the excitability distribution is cell-intrinsic or a network phenomenon, and the degree to which this excitability is fixed or possibly time-varying are open questions for future studies. © 2016 Wiley Periodicals, Inc.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".