MECHANISMS OF TOPOGRAPHIC MAP DEVELOPMENT WITHIN THE MAMMALIAN VISUAL SYSTEM
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".