Attractor-Map Versus Autoassociation Based Attractor Dynamics in the Hippocampal Network
Bibliographic record
Abstract
The autoassociative memory model of hippocampal field CA3 postulates that Hebbian associations among external input features produce attractor states embedded in a recurrent synaptic matrix. In contrast, the attractor-map model postulates that a two-dimensional continuum of attractor states is preconfigured in the network during development and that transitions among these states are governed primarily by self-motion information ("path-integration"), giving rise to the strong spatial characteristic of hippocampal activity. In this model, learned associations between "coordinates" on the attractor map and external cues can result in abrupt jumps between states, in the case of mismatches between the current input and previous associations between internal coordinates and external landmarks. Both models predict attractor dynamics, but for fundamentally different reasons; however, the two models are not a priori mutually exclusive. We contrasted these two models by comparing the dynamics of state transitions when two previously learned environmental shapes were morphed between their endpoints, in animals that had first experienced the environments either at the same location, or at two different locations, connected by a passageway through which they walked. As predicted from attractor-map theory, the latter animals expressed abrupt transitions between representations at the midpoint of the morph series. Contrary to the predictions of autoassociation theory, the former group expressed no evidence of attractor dynamics during the morph series; there was only a gradual transition between endpoints. The results of this critical test thus cast the autoassociator theory for CA3 into doubt and indicate the need for a new theory for this structure.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".