The phenomenon of receptive field remapping and its neural mechanism
Bibliographic record
Abstract
Eye movements cause displacement and smearing of images on the retina. Yet, we experience a stable and seamless visual sensation as if eye movements never occurred. This is because the brain has a mechanism for compensating for self-generated movements. This thesis is a compiled series of findings obtained by investigating such a compensatory mechanism of mammalian brain in the context of saccadic eye movements. The doctoral work specifically focuses on a neural phenomenon called receptive field remapping. Remapping refers to a property of certain visual neurons that can predictively respond to visual stimuli outside their classical response field whenever there is an impending eye movement. Chapter 1 provides a literature review of receptive field remapping. The chapter ends with an outline of the objectives of this doctoral work which are to characterize the phenomenon of receptive field remapping to reconcile apparent discontinuity in the literature and to investigate the mechanism of remapping. Chapter 2 contains the results from the first study undertaken during the PhD work. It shows that there are two types of remapping in primate cortical area V4 and provides a ground to reconcile recently published incompatible findings on the phenomenon of remapping. Chapter 3 provides further evidence for dissociating the two types of remapping obtained by carefully choosing a saccade vector based on the receptive field location of the neurons under study. Chapter 4 contains the results from a study undertaken during this PhD work to understand the neural mechanism of remapping. Using simultaneous recording from multiple neurons, this work shows the role of coherent oscillations in remapping thus providing evidence for a dynamic mechanism of activity transfer during saccades. Overall, the results presented in this thesis helps reconcile recently surfaced apparent controversy on the phenomenon of remapping and provides evidence for communication through coherence as a putative neural mechanism of remapping.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".