Dynamic Tuning of Electrical and Chemical Synaptic Transmission in a Network of Motion Coding Retinal Neurons
Bibliographic record
Abstract
Recently, we demonstrated that gap junction coupling in the population of superior coding ON-OFF directionally selective ganglion cells (DSGCs) genetically labeled in the Hb9::eGFP mouse retina allows the passage of lateral anticipatory signals that help track moving stimuli. Here, we examine the properties of gap junctions in the DSGC network, and address how interactions between electrical and chemical synapses and intrinsic membrane properties contribute to the dynamic tuning of lateral anticipatory signals. When DSGC subtypes coding all four cardinal directions were individually loaded with the gap junction-permeable tracer Neurobiotin, only superior coding DSGCs exhibited homologous coupling. Consistent with these anatomical findings, gap junction-dependent feedback spikelets were only observed in Hb9(+) DSGCs. Recordings from pairs of neighboring Hb9(+) DSGCs revealed that coupling was reciprocal, non-inactivating, and relatively weak, and provided a substrate for an extensive subthreshold excitatory receptive field around each cell. This subthreshold activity appeared to boost coincident light-driven chemical synaptic responses. However, during responses to moving stimuli, gap junction-mediated boosting appeared to be dynamically modulated such that upstream DSGCs primed downstream cells, but not vice versa, giving rise to highly skewed responses in individual cells. We show that the asymmetry in priming arises from a combination of spatially offset GABAergic inhibition and activity-dependent changes in intrinsic membrane properties of DSGCs. Thus, dynamic interactions between electrical and chemical synapses and intrinsic membrane properties allow the network of DSGCs to propagate anticipatory responses most effectively along their preferred direction without leading to runaway excitation.
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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) | 0.000 | 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".