Proactive Inhibitory Control and Attractor Dynamics in Countermanding Action: A Spiking Neural Circuit Model
Bibliographic record
Abstract
Flexible behavior depends on the brain's ability to suppress a habitual response or to cancel a planned movement whenever needed. Such inhibitory control has been studied using the countermanding paradigm in which subjects are required to withhold an imminent movement when a stop signal appears infrequently in a fraction of trials. To elucidate the circuit mechanism of inhibitory control of action, we developed a recurrent network model consisting of spiking movement (GO) neurons and fixation (STOP) neurons, based on neurophysiological observations in the frontal eye field and superior colliculus of behaving monkeys. The model places a premium on the network dynamics before the onset of a stop signal, especially the experimentally observed high baseline activity of fixation neurons, which is assumed to be modulated by a persistent top-down control signal, and their synaptic interaction with movement neurons. The model simulated observed neural activity and fit behavioral performance quantitatively. In contrast to a race model in which the STOP process is initiated at the onset of a stop signal, in our model whether a movement will eventually be canceled is determined largely by the proactive top-down control and the stochastic network dynamics, even before the appearance of the stop signal. A prediction about the correlation between the fixation neural activity and the behavioral outcome was verified in the neurophysiological data recorded from behaving monkeys. The proposed mechanism for adjusting control through tonically active neurons that inhibit movement-producing neurons has significant implications for exploring the basis of impulsivity associated with psychiatric disorders.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".