An Intrinsic Neural Oscillator in the Degenerating Mouse Retina
Bibliographic record
Abstract
The loss of photoreceptors during retinal degeneration (RD) is known to lead to an increase in basal activity in remnant neural networks. To identify the source of activity, we combined two-photon imaging with patch-clamp techniques to examine the physiological properties of morphologically identified retinal neurons in a mouse model of RD (rd1). Analysis of activity in rd1 ganglion cells revealed sustained oscillatory (∼10 Hz) synaptic activity in ∼30% of all classes of cells. Oscillatory activity persisted after putative inputs from residual photoreceptor, rod bipolar cell, and inhibitory amacrine cell synapses were pharmacologically blocked, suggesting that presynaptic cone bipolar cells were intrinsically active. Examination of presynaptic rd1 ON and OFF bipolar cells indicated that they rested at relatively negative potentials (less than -50 mV). However, in approximately half the cone bipolar cells, low-amplitude membrane oscillation (∼5 mV, ∼10 Hz) were apparent. Such oscillations were also observed in AII amacrine cells. Oscillations in ON cone bipolar and AII amacrine cells exhibited a weak apparent voltage dependence and were resistant to blockade of synaptic receptors, suggesting that, as in wild-type retina, they form an electrically coupled network. In addition, oscillations were insensitive to blockers of voltage-gated Ca(2+) channels (0.5 mm Cd(2+) and 0.5 mm Ni(2+)), ruling out known mechanisms that underlie oscillatory behavior in bipolar cells. Together, these results indicate that an electrically coupled network of ON cone bipolar/AII amacrine cells constitutes an intrinsic oscillator in the rd1 retina that is likely to drive synaptic activity in downstream circuits.
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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 | 0.001 | 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 | 0.001 |
| 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".