Bibliographic record
Abstract
Animals receive information about the external world through their senses, which feed this information into the nervous system where it’s processed, stored, and subsequently influences behaviour. In this work, I describe both how the first steps of chemosensory processing occur in the sensory neurons of the nematode Caenorhabditis elegans, and how the information provided by this chemosensory input, in conjunction with previous sensory information and the internal satiety state of the animal, act to modulate subsequent behaviour. In the first part of this work, I provide an analysis of the phenomenon of Kamin Blocking in C. elegans, in which past learning about sensory cues can modulate the ability of subsequent cues to form memories, and offer a molecular analysis of Kamin Blocking in the worm which challenges a common interpretation of it in mammals. In the second part of this work, I focus on the very initial steps occurring during chemosensation in C. elegans chemosensory neurons, and find that discrimination between different olfactory stimuli which are sensed within the same neuron relies on β-arrestin mediated desensitization of ligand-bound receptors. I provide a model for how desensitization acts to enable intraneuronal olfactory discrimination in the paradigm being tested, offering a resolution to a longstanding question in the field, and test several predictions of this model. In sum, the research presented in this work describes both the earliest mechanisms of chemosensory biology in the worm, and examines how chemosensation acts to create memories and modulate subsequent behaviour. By exploiting the simplicity and genetic tractability of C. elegans, the research described here deepens our understanding of the mechanisms underlying (and linking) the most fundamental aspects of sensation and learning in animals.
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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 | 0.001 |
| 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".