Long‐term recognition memory of individual conspecifics is associated with telencephalic expression of Egr‐1 in the electric fish <i>Apteronotus leptorhynchus</i>
Bibliographic record
Abstract
Primates and songbirds can learn to recognize individual conspecifics based on complex sensory cues; this requires a large, highly differentiated dorsal telencephalon. Here we show that the electric fish Apteronotus leptorhynchus can learn to recognize individual conspecifics based on a simple cue, the beat frequency of their summed sinusoidal electric organ discharges (EOD). Male fish produce transient communication signals (chirps) in response to mimic EODs. The chirp response habituates over repeated stimulus presentations within one experimental session, continues to habituate over successive daily sessions and is nearly extinguished after 5-7 days. Habituation of the chirp response was specific to the presented beat frequency. The conversion of short- to long-term habituation could be disrupted by cooling the head 30 minutes after the daily habituation trials. Consolidation of long-term memory in mammals is thought to involve induced expression of an immediate early gene, Egr-1. We cloned the Apteronotid homolog of the Egr-1 gene and found that chirp-evoking stimuli induced strong expression of its mRNA within the dorsal (Dd), central (DC), and lateral (DL) subdivisions of the dorsal telencephalon. Interestingly, the dorsolateral region is hypothesized to be homologous to the amniote hippocampal formation. We conclude that A. leptorhynchus can learn to identify individual conspecifics based on their EOD frequency and can remember these frequencies for several days. We hypothesize that this form of learning, as in primates and songbirds, requires a subset of dorsal telencephalic areas and involves a consolidation-like process that includes the expression of the transcription factor AptEgr-1.
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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) | 0.001 | 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".