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Record W4210615170 · doi:10.1101/2022.02.01.478650

Dissociable Roles of FEF Neurons in Initiating Saccades

2022· preprint· en· W4210615170 on OpenAlexaff
Mohammad Shams-Ahmar, Peter Thier, Yaser Merrikhi

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsMcGill University
Fundersnot available
KeywordsSaccadeNeuroscienceContext (archaeology)Variable (mathematics)PsychologyNeuronEye movementComputer scienceCognitive psychologyBiologyMathematics

Abstract

fetched live from OpenAlex

Abstract The frontal eye field (FEF) plays a key role in initiating saccades. To explain how single neurons in this region may enable the initiation of saccades, Jeffery Schall proposed the “variable rate model” (Hanes and Schall, 1996) which assumes that the discharge of a neuron must reach a fixed activation threshold for a saccade to be evoked. However, the validity of this model has been questioned as results from later work testing saccades in different behavioral contexts seemed to require the assumption of variable thresholds. Moreover, even when sticking to the original behavioral context, not all types of frontal eye field neurons seemed to behave according to a variable rate model. In an attempt to reconsider the viability of the variable rate model avoiding limitations of previous research, we studied single neurons recorded from the FEF of two rhesus monkeys while they performed a memory-guided saccade task. We evaluated the degree to which each type of FEF neurons complied with the variable rate model by quantifying how precisely their discharge predicted an imminent saccade based on their immediate presaccadic activity. In addition, we asked whether there might be a hierarchical relation between visuomotor and motor neurons with only the latter responsible for the release of saccades and therefore satisfying the assumption of the variable rate model. We show that decoders trained on single motor neurons’ presaccadic activity performed better than decoders trained on visuomotor neurons in predicting an imminent saccade, in line with the assumption of a top position in a putative processing hierarchy. While this position might have suggested significant resilience to perturbation of the visual input to the FEF, we found quite the opposite that motor neurons but not visuomotor neurons were susceptible to perturbation. As a matter of fact, they lost the ability to predict the initiation of a saccade based on their immediate pressacadic discharge rate. Our results support the notion that motor neurons and visuomotor neurons have dissociable functions in information processing for saccades with motor neurons more closely related to saccade initiation, in line with the tenets of the variable rate model. Yet, they also clearly indicate that even these supposedly top layer neurons exhibit an unexpected degree of dependence on afferent input questions the viability of the assumption of a simple processing hierarchy.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.240
Teacher spread0.215 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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