Model-based functional segmentation of the human lateral geniculate nucleus
Bibliographic record
Abstract
The lateral geniculate nucleus of the thalamus (LGN) is somewhat unique in the visual pathway in that there is a clear separation of structure and function at a spatial scale that is resolvable by contemporary functional imaging techniques. Therefore, it provides a unique opportunity for developing and testing models of neural function, visual perception, and information flow throughout the brain. For instance, one prevailing theory of dyslexia contends that a malfunction in the M system throughout the brain is responsible for the behavioral deficits observed in dyslexics (Stein, 2001; Stein and Walsh, 1997). The LGN receives input from retinal ganglion cells and projectors directly to primary visual cortex, and can be functionally subdivided into layers on the basis of the response properties of the neurons contained therein. Neurons in the magnocellular (M) and parvocellular (P) layers of the LGN have distinct and complimentary spatial and temporal tuning properties. However, functionally segmenting the LGN on the basis of these neural response properties using functional brain imaging techniques has proven difficult. Recent attempts have been made to segment the LGN into its M and P subdivisions (Denison et al., 2014; Zhang et al., 2015) using fMRI. In these experiments, researchers took advantage of the complementarity of the response properties of M and P neurons to differentially drive the BOLD activity during the presentation of various stimulus features (i.e., contrast, spatial frequency, temporal frequency, color sensitivity). Here, we present a new spatiotemporal population receptive field (pRF) model that leverages the differences in the temporal frequency tuning and neural discharge patterns among M and P neural populations. This spatiotemporal pRF model estimates provide activation maps describing the LGN both in terms of its retinotopic organization and temporal response profile, and so affords an avenue for differentiating the M and P layers of the LGN. Meeting abstract presented at VSS 2017
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".