Intrinsic Frequency Tuning in ELL Pyramidal Cells Varies Across Electrosensory Maps
Bibliographic record
Abstract
The tuning of neuronal responsiveness to specific stimulus frequencies is an important computation across many sensory modalities. The weakly electric fish Apteronotus leptorhynchus detects amplitude modulations of a self-generated quasi-sinusoidal electric organ discharge to sense its environment. These fish have to parse a complicated electrosensory environment with a wide range of possible frequency content. One solution has been to create multiple representations of the sensory input across distinct maps in the electrosensory lateral line lobe (ELL) that participate in distinct behavioral functions. E- and I-type pyramidal cells in the ELL that process sensory input further exhibit a preferred range of stimulus frequencies in relation to the different behaviors and sensory maps. We tested the hypothesis that variations in the intrinsic spiking mechanism of E- and I-type pyramidal cells contribute to map-specific frequency tuning. We find that E-cells exhibit a systematic change in their intrinsic spike characteristics and frequency tuning across sensory maps, whereas I-cells are constant in both spike characteristics and frequency tuning. As frequency tuning becomes more high-pass in E-cells, the refractory variables of spike half-width and afterhyperpolarization magnitude increase, spike threshold increases, adaptation becomes faster, and the gain of the spiking response decreases. These findings indicate that frequency tuning across sensory maps in the ELL is supported by differences in the intrinsic spike characteristics of pyramidal cells, revealing a link between cellular biophysical properties and signal processing in sensory maps with defined behavioral roles.
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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".