Encoding mechanisms for sensory neurons studied with a multielectrode array in the cat dorsal root ganglion
Bibliographic record
Abstract
Recent advances in microelectrode array technology now permit a direct examination of the way populations of sensory neurons encode information about a limb's position in space. To address this issue, we recorded nerve impulses from about 100 single units simultaneously in the L6 and L7 dorsal root ganglia (DRG) of the anesthetized cat. Movement sensors, placed near the hip, knee, ankle, and foot, recorded passive movements of the cat's limb while it was moved pseudo-randomly. The firing rate of the neurons was correlated with the position of the limb in various coordinate systems. The firing rates were less correlated to the position of the foot in Cartesian coordinates (x, y) than in joint angular coordinates (hip, knee, ankle), or in polar coordinates. A model was developed in which position and its derivatives are encoded linearly, followed by a nonlinear spike-generating process. Adding the nonlinear portion significantly increased the correlations in all coordinate systems, and the full models were able to accurately predict the firing rates of various types of sensory neurons. The observed residual variability is captured by a simple stochastic model. Our results suggest that compact encoding models for primary afferents recorded at the DRG are well represented in polar coordinates, as has previously been suggested for the cortical and spinal representation of movement. This study illustrates how sensory receptors encode a sense of limb position, and it provides a general framework for modeling sensory encoding by populations of neurons.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".