Effects of phase correlations in naturalistic stimuli on quantitative information coding by fly photoreceptors
Bibliographic record
Abstract
Natural visual scenes are rarely random. Instead, intensity and wavelength change slowly in time and space over many regions of the scene, so that neighboring temporal and spatial visual inputs are more correlated and contain less information than truly random signals. It has been suggested that sensory optimization to match these higher order correlations (HOC) occurs at the earliest visual stages, and that photoreceptors can process temporal natural signals more efficiently than random signals. We tested this early-stage hypothesis by comparing the information content of Calliphora vicina photoreceptor responses to naturalistic inputs before and after removing HOC by randomizing phase. Forty different, 60-s long, naturalistic sequences (NS) were used, together with randomized-phase versions of the same sequences to give pink noise (PN) so that each input pair had identical means, variances, mean contrasts, and power spectra. We measured the information content of inputs and membrane potential responses by three different methods: coherence, mutual information, and compression entropy. We also used entropy and phase statistics of each pair as measures of HOC. Responses to randomized signals generally had higher gain, signal-to-noise ratio, and information rates than responses to NS. Information rate increased with a strong, positive, linear correlation to phase randomization within sequence pairs. This was confirmed by varying the degree of phase randomization. Our data indicate that individual photoreceptors encode input information by Weber's law, with HOC within natural sequences reducing information transfer by decreasing the number of local contrast events that exceed the noise-imposed threshold. NEW & NOTEWORTHY Natural visual scenes feature statistical regularities, or higher order correlations (HOC), both in time and space, to encode surfaces, textures, and object boundaries. Visual systems rely on this information; however, it remains controversial whether individual photoreceptors can discriminate and enhance information encoded in HOC. Here we show that the more HOC the stimulus contains, the lower the information transfer rate of photoreceptors. We explain our findings by applying the Weber's paradigm of differential signal perception.
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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 | 0.002 |
| 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".