Identification of Basolateral Amygdala Projection Cells and Interneurons Using Extracellular Recordings
Bibliographic record
Abstract
This study tested whether firing rate and spike shape could be used to distinguish projection cells from interneurons in extracellular recordings of basolateral amygdala (BLA) neurons. To this end, we recorded BLA neurons in isoflurane-anesthetized animals with tungsten microelectrodes. Projection cells were identified by antidromic activation from cortical projection sites of the BLA. Although most projection cells fired spontaneously at low rates (<1 Hz), an important subset fired at higher rates (up to 6.8 Hz). In fact, the distribution of firing rates in projection cells and unidentified BLA neurons overlapped extensively, even though the latter cell group presumably contains a higher proportion of interneurons. The only difference between the two distributions was a small subset (5.1%) of unidentified neurons with unusually high firing rates (9-16 Hz). Similarly, distributions of spike durations in both cell groups were indistinguishable, although most of the fast-firing neurons had spike durations at the low end of the distribution. However, we observed that spike durations depended on the exact position of the electrode with respect to the recorded cell, varying by as much as 0.7 ms. Thus neither firing rate nor spike waveform allowed for unequivocal separation of projection cells from interneurons. Nevertheless, we propose the use of two firing rate cutoffs to obtain relatively pure samples of projection cells and interneurons: < or =1 Hz for projection cells and > or =7 Hz for fast-spiking interneurons. Supplemented with spike-duration cutoffs of > or =0.7 ms for projection cells and < or =0.5 ms for interneurons, this approach should keep instances of misclassifications to a minimum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".