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Record W4366998751 · doi:10.5463/thesis.193

C. elegans behaviour and brain dynamics; a physical exploration

2023· dissertation· en· W4366998751 on OpenAlexaff
Willem Mathijs Rozemuller

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Toronto
FundersWageningen University and Research
KeywordsVariation (astronomy)Perspective (graphical)Cognitive scienceConstruct (python library)Mechanism (biology)BiologyRange (aeronautics)Data scienceArtificial intelligenceCognitive psychologyEvolutionary biologyEcologyPsychologyComputer scienceEpistemologyEngineering

Abstract

fetched live from OpenAlex

How and why animals exhibit certain behaviours is one of the most interesting, but also among the most complex biological questions to answer. Traditionally, this question was approached from either ethology -- focusing on strategy (the why) from an evolutionary perspective, or neuroscience -- focusing on mechanism (the how) from a physiological perspective, but the gap between these approaches is now narrowing with technological advances allowing the collection of vast data sets to capture motile behaviour and brain dynamics, including its diversity and variability. In this thesis, we have taken a physical approach towards understanding motile behaviour, striving to uncover simple ideas from large, holistic data sets in a principled way, by applying dimensionality reduction and minimalistic modelling to the nematode Caenorhabditis elegans, a 1 mm-long nematode with just 302 neurons. After a brief introduction in Chapter 1, in chapter 2 we develop a quantitative and predictive description of motile behaviour and use it to study behaviour of a wide range of nematode species. Therefore, we construct a minimal 7-parameter model that captures the essential behaviours: speed, rotational, and reversal dynamics, including their fluctuations. We find that this model captures variation across individuals and a broad range of species within the Nematoda phylum. Interestingly, behaviour varies most prominently across a common mode, and moving along this mode strongly changes the exploratory propensity towards more roaming or more dwelling. In addition, variation across individuals is comparable to variation across species, which suggests a common underlying pathway. Chapter 3 focuses on the turning aspect of behaviour and asks what are the modes of control, and how the system is optimised to mitigate limits of control and intrinsic biases. We realise this by extracting and analysing postures from a large number of worms performing exploratory and escape tasks during two-hour recordings, leveraging naturally occurring variability in turning statistics across time and individuals under similar conditions. The results show that during exploration, worms exhibit a slowly fluctuating but persistent gradual rotational bias, curtailing their exploratory propensity. However, with a simple model, we show that the effective rate of random reorientation, on average, minimises the negative impact of the rotational bias, which could reflect a constrained optimization. Finally, we show how during escape responses, the worm exerts control over its sharp turn statistics, with respect to both direction and amplitude, to overcome its intrinsic biases that would be detrimental to escape. In Chapter 4 we investigate the neuronal signalling that underlies behaviour and its relationship to motility and sensory inputs. We develop a measurement-analysis pipeline that enables long timescale whole-brain recordings of brain dynamics with a simple one-dimensional behavioural output and the ability to provide temporally controlled chemical stimuli. With this system we investigate 3 hypotheses about C. elegans brain dynamics: (1) the collective motor-command hypothesis, which states that correlated activity of many neurons serve as motor commands, (2) the apparent stochasticity hypothesis, which states that individual neuron activity can appear stochastic due to influences of brain-wide dynamics, and (3) the neuromodulated brain-states hypothesis, which states that brain-wide activity states, such as sleep, can be triggered by neuromodulatory chemicals. Our results yield positive evidence in support of each of these hypothesis. The results presented in this thesis contribute to closing the aforementioned gap between understanding the how and why of behaviour. On the one hand, tools have been developed to quantify, model, and analyse motile behaviour in the absence and presence of stimuli and in the context of behavioural strategies. On the other hand, we have made inroads to studying how the brain encodes and processes information, under the influence of the same stimuli.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.267
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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