A new functional role for lateral inhibition in the striatum: Pavlovian conditioning
Bibliographic record
Abstract
Abstract The striatum has long been implicated in reinforcement learning and has been suggested by several neurophysiological studies as the substrate for encoding the reward value of stimuli. Reward prediction error (RPE) has been used in several basal ganglia models as the underlying learning signal, which leads to Pavlovian conditioning abilities that can be simulated by the Rescorla-Wagner model.Lateral inhibition between striatal projection neurons was once thought to have a winner-take-all function, useful in selecting between possible actions. However, it has been noted that the necessary reciprocal connections for this interpretation are too few, and the relative strength of these synaptic connections is weak. Still, modeling studies show that lateral inhibition does have an overall suppression effect on striatal activity and may play an important role in striatal processing.Neurophysiological recordings show task-relevant ensembles of responsive neurons at specific points in a behavioral paradigm (Barnes et al., 2005), which appear to be induced by lateral inhibition (see Ponzi and Wickens, 2010). We have developed a similarly responding, RPE-based model of the striatum by incorporating lateral inhibition. Model neurons are assigned to either the direct or the indirect pathway but lateral connections occur within and between these groups, leading to competition between both the individual neurons and their pathways. We successfully applied this model to the simulation of Pavlovian phenomena beyond those of the Rescorla-Wagner model, including negative patterning, unovershadowing, and external inhibition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.002 |
| 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 teacher head, 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".