Functional connectivity during surround suppression in macaque area V4
Bibliographic record
Abstract
Surround suppression is a common feature of sensory neurons. For neurons of the visual cortex, it occurs when a visual stimulus extends beyond a neuron's classical receptive field, reducing the neuron's firing rate. While several studies have been attributing the suppression effect on horizontal, long-range lateral or feedback connections, the underlying circuitry for surround modulation remain unidentified. Since most of these models have been relying on single neuron recordings, the contribution of lateral connections can only be suggested from the surround field properties. A more straightforward approach would be to detect these connections and their dynamics using simultaneous recordings from multiple neurons in one or more visual areas. We have developed a method for estimating these connections and we analyzed data obtained from 100-electrode Utah arrays chronically implanted into area V4 of the macaque monkey. Using a method based on the nonlinear Volterra modeling approach, we computed estimates of the strength and statistical reliability of connections among neurons, including nonlinear interactions and excitatory and inhibitory connections. Our results thus far reveal a pattern of connectivity within V4 that conforms to the results of previous anatomical work: Excitatory connections are far more common than inhibitory connections (∼65%), stronger connections are found among neurons that are physically near one another, and connections are stronger among neurons with similar receptive field properties. However, this connectivity is capable of reorganizing on short time scales according to the stimulus: Stimuli that evoke strong suppression at the single-unit level introduce stronger inhibition among V4 neurons, identifying recurrent connectivity as the source of the suppression. Overall, these results provide insight into the dynamic nature of neuronal organization within V4 and its contribution to surround suppression.
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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 | 0.001 | 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".