Distinct local circuit properties of the superficial and intermediate layers of the rodent superior colliculus
Bibliographic record
Abstract
The superior colliculus (SC) is critical in localizing salient visual stimuli and making decisions on the location of the next saccade. Lateral interactions across the spatial map of the SC are hypothesized to help mediate these processes. Here, we investigate lateral interactions within the SC by applying whole-cell recordings in horizontal slices of mouse SC, which maintained the local structure of the superficial (SCs) visual layer, which is hypothesized to participate in localizing salient stimuli, and the intermediate (SCi) layer, which is supposed to participate in saccade decision-making. When effects of either electrical or chemical (uncaging of free glutamate) stimuli were applied to multiple sites with various distances from the recorded cell, a pattern of center excitation-surround inhibition was found to be prominent in SCs. When the interactions of synaptic effects induced by simultaneous stimulation of two sites were tested, non-linear facilitatory or inhibitory interactions were observed. In contrast, in the SCi, stimulation induced mainly excitation, which masked underlying inhibition. The excitatory synaptic effects of stimulation applied at remote sites were summed in a near linear manner. The result suggested that SCs lateral interactions appear suitable for localizing salient stimuli, while the lateral interactions within SCi are more suitable for faithfully accumulating subthreshold signals for saccadic decision-making. Implementation of this laminar-specific organization makes the SC a unique structure for serially processing signals for saliency localization and saccadic decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".