A phenomenological model for self-initiated movement in electric fish
Notice bibliographique
Résumé
Observing behaviourally unconstrained animals can lead to simple characterization of complex behaviour. We apply this principle to infer the neural dynamics of self-initiated movement in pulse-type electric fish, Gymnotus sp. Recent long-term monitoring of fish (~200 hours from 22 recording sessions, over 4 animals) in freely swimming conditions, devoid of external stimuli, reveals non-trivial structures in their pattern of electric organ discharge (EOD). Simultaneous recording of EODs and fish movement show that the EOD rate (EODR) and the activity level of the fish are bi-modally distributed, as well as highly correlated. These features thus effectively define behavioural attractor states corresponding to high and low levels of neural activity (up- and down-states, respectively). Trajectories in the EODR-activity plane consist of diffusional motion around the attractor states, interrupted by sharp transitions between states. The duration of each state is uncorrelated with that of the next up- or down-state, and is log-normally distributed with no characteristic time-scale. Based on this data, our goal is to develop a modelling framework to better understand the neural pathway responsible for self-initiated movement. However, because the physiological parameters defining this pathway are experimentally unconstrained, it would be premature at this point to develop a detailed biophysical model of this system. There is thus a preliminary need to, instead, characterize the key features of the data from a phenomenological perspective. To address this research gap, we attempt to fit a stochastic process, with the simplest combination of dynamical components, that most closely reproduces the statistics of the data. As a first step, we hypothesize that the first principal component of the data (EODR and activity level) follows an overdamped Brownian motion in a double-well potential with additive noise. Based on the approach of [1], we fit a 4th order polynomial for the potential function by associating the stationary solution of the Fokker-Planck equation with the measured histogram. For most recording sessions, the fits appropriately reproduce the histograms, but consistently underestimates the width of the up-state potential well. Once the potential function is determined, we generate an estimate for the noise intensity by calculating the mean escape time from small regions at the bottom of either well, and comparing it with its theoretical expression. We find, however, that this estimate is dependent on the well that was used to generate it, with all recording sessions showing a larger noise estimate for the up-state than for the down-state. Moreover, in most cases, the ratio of up- to down-state duration is underestimated by the fitted process, indicative of either too short up-states, or too long down-states compared to the data. These findings are consistent with the above discrepancy for the width of the up-state potential well, as well as with visual inspection of the data, which shows greater variability when fish are in up-states. Taken together, these observations strongly suggest that state-dependent noise is involved in the process generating the data, either in the form of multiplicative noise, or Poisson shot noise.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».