Sparse coding of natural communication signals in midbrain neurons
Bibliographic record
Abstract
Sparse neural codes (i.e.codes in which neurons respond only to a few stimuli) have been widely observed across animal taxa [1][2][3][4].Theoretical studies suggest that sparse neural codes critically depend on non-linear mechanisms [5].However, the network and cellular properties that enable the observed sparse responses remain unclear.We investigated sparse coding in neurons within the midbrain torus semicircularis (TS) in the weakly electric fish Apteronotus leptorhynchus, which is equivalent to the inferior colliculus in the mammal.These fish generate a quasisinusoidal electric field via the electric organ discharge (EOD) with a characteristic frequency that varies across individuals.When two individuals come into contact, interference between their EODs will give rise to a beat phenomenon.Male Apteronotus leptorhynchus will transiently increase their EOD frequencies in a stereotypical manner during agonist encounters or courtship rituals: these chirps will occur in conjunction with the beat and must be distinguished by either the other male or the female (Figure 1).We performed in vivo patch clamp recordings to study TS neural responses to chirps occurring on top of the beat pattern.We found that one neuron type responded almost exclusively to chirps in a most peculiar manner.These neurons had little or no activity during the beat and fired a single action potential in response to the chirp (Figure 2).Chirp detection was negatively correlated with phase locking to the beat suggesting a segregation of information flow in midbrain neurons.Moreover, the chirp detection abilities were highly superior to those found in neurons afferent to TS.
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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 | 0.001 |
| 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 | 0.000 | 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".