MECHANISMS OF TOPOGRAPHIC MAP DEVELOPMENT WITHIN THE MAMMALIAN VISUAL SYSTEM
Notice bibliographique
Résumé
The brain relies on stereotypical patterns of axonal connections to efficiently process sensory information. These patterns of organized connections or neural circuits are formed during development, under the control of multiple axon guidance pathways. They are thought to include both cell-cell interactions and cell-autonomous mechanisms. The organization of these patterned connections can take various forms, which can be classed generally as discrete maps, continuous maps, or a combination of both. The connections formed by olfactory neurons in individual olfactory bulb glomeruli are a prototype of discrete mapping – where the axon termination zones are grouped by their chemical response properties. Conversely, axons in the auditory system are arranged in a continuous map according to their frequency response. These kinds of continuous maps – where Euclidean geometry is preserved locally – are known as topographic maps. Axons from retinal ganglion cells in the retina and sensory neurons in the skin also form continuous maps. One of the most well studied brain regions for neural circuit development is the superior colliculus. Located in the midbrain, it is known for its role in multisensory integration. There, it receives axonal projections from various sense organs and integrates them into outputs to control behaviour. The collection of these incoming sensory projections is organized such that salient sensory information can be preserved and processed meaningfully. This is to say, the physical 2D arrangement of the retinal surface is preserved in the collection of its axonal projections. In addition to the retinal topographic map, there is also a second visual topographic map in the superior colliculus, formed by axons projected from the primary visual cortex. These two topographic maps are aligned such that the incoming visual information from each is spatially aligned and coordinated. The mechanisms that control the formation of these orderly connections will be the focus of this investigation. As the factors controlling the formation of topographic maps are so complicated, and the volume of data generated on the subject is so vast, it is important to employ quantitative models to generate specific, testable predictions about the mapping process. A great deal of effort has gone into generating computational models of topographic mapping. One model that best describes a wide variety of experimental conditions was presented by Tsigankov and Koulakov (2006). This model of topographic map refinement has since been further expanded and tested to propose a mechanism for the topographic alignment between the RC and CC maps – based on the concepts of both spontaneous activity and chemical signalling cues. Known as the 3 Step Map Alignment Model, this proposed mechanism will be further refined using specific experimental data and probed for novel experimental predictions. Further building on the work based on Isl2-EphA3 KI animals and Isl2-efnA3 KI animals, a novel animal model has been generated to assist in testing predictions made by this computational modelling. This model instead places Cre under control of Isl2, ready to be combined with standard mutant animals containing target genes with flanking loxP sites, in order to easily generate conditional knockout animals. This mutant animal will be validated by crossing it with the standard Cre-reporter line, Ai9. However, the presence of stochastic expression events in some of the animals appear to limit the construct’s utility.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».