Receptive field organization across multiple electrosensory maps. II. Computational analysis of the effects of receptive field size on prey localization
Bibliographic record
Abstract
The electric fish Apteronotus leptorhynchus emits a high-frequency electric organ discharge (EOD) sensed by specialized electroreceptors (P-units) distributed across the fish's skin. Objects such as prey increase the amplitude of the EOD over the underlying skin and thus cause an increase in P-unit discharge. The resulting localized intensity increase is called the electric image and is detected by its effect on the P-unit population; the electric image peak value and the extent to its spreads are cues utilized by these fish to estimate the location and size of its prey. P-units project topographically to three topographic maps in the electrosensory lateral line lobe (ELL): centromedial (CMS), centrolateral (CLS), and lateral (LS) segments. In a companion paper I have calculated the receptive fields (RFs) in these maps: RFs were small in CMS and very large in LS, with intermediate values in CLS. Here I use physiological data to create a simple model of the RF structure within the three ELL maps and to compute the response of these model maps to simulated prey. The Fisher information (FI) method was used to compute the optimal estimates possible for prey localization across the three maps. The FI predictions were compared with behavioral studies on prey detection. These comparisons were used to frame alternative hypotheses on the functions of the three maps and on the constraints that RF size and synaptic strength impose on weak signal detection and estimation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".