KINARM, EyeLink & Cerebus - Hardware Setup, Data Flow & Task Environment
Bibliographic record
Abstract
Vision-for-action is defined as a distinct functional stream of brain processes which allow us to use of complex perceptual processing for goal-directed actions. However, it is still unclear how the widely distributed neuronal networks are organized to allow the visual and motor cortical areas to precisely coordinate the moment-to-moment information about the visual environment relative to the observer. This study aims to decipher the electrophysiological correlates connecting these distinct cortical areas by investigating the coordination of primary input and output areas in different behavioral conditions. Two 100-electrode micro-arrays will be chronically implanted in the primary visual (V1) and motor (M1) cortical areas of macaque monkeys to simultaneously record both single neuron spiking and population (local field potential, LFP) activities during a visually guided tracking task. The monkey is positioned into a exoskeleton robot (KINARM, BKin Technologies Ltd., Kingston, ON, Canada, www.bkintechnologies.com), which allows a full range of movements of one arm on a 2-dimensional horizontal plane. A semi-transparent mirror projects a virtual representation of his hand (a "dot") in the plane of the real (but invisible) arm together with the visual stimulus guiding the movement. The behavioral paradigm is designed so that visuo-motor coordination is required at some times, and not at others and contains various conditions such as (i) smooth tracking of various visual stimuli; (ii) modifying the visual environment to bias visual perception; (iii) perturbing mechanically the movement trajectory by using opposing force fields in an expected or unexpected manner; (iv) visual tracking vs. self-paced movements; (v) dissociating the visual environment from movement execution by modifying the gain of the visual feedback signal; etc. In all conditions, visual feedback of the movement trajectory may or may not be provided to the monkey. This study will provide new insights in selective cooperativity patterns within intra- and inter-areal networks. The initial pilot study will focus on programming efficient training protocols which prove to be successful for macaque monkeys, and allow the conditioned responses to manifest themselves. Once the desired behavior is established and the Utah arrays are implanted, the electrophysiological data will be collected and analyzed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.029 |
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".