Closed-loop control and recalibration of place cells by optic flow
Bibliographic record
Abstract
Abstract Understanding the interplay between sensory input, endogenous neural dynamics, and behavioral output is key toward understanding the principles of neural computation. Hippocampal place cells are an ideal system to investigate this closed-loop interaction, as they are influenced by both self-motion (idiothetic) signals and by external sensory landmarks as an animal navigates its environment 1–9 . To continuously update a position signal on an internal “cognitive map”, the hippocampal system integrates self-motion signals over time 10,11 . In the absence of stable, external landmarks, however, these spatial correlates of neuronal activity can quickly accumulate error and cause the internal representation of position or direction to drift relative to the external environment 1,5 . We have previously demonstrated that, in addition to their known roles in preventing and/or correcting path-integration error, external landmarks can be used as a putative teaching signal to recalibrate the gain of the path integration system 6 . However, it remains unclear whether idiothetic cues, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if instead an unambiguous allocentric frame of reference, anchored by polarizing landmark information, is essential for path integration recalibration. Here, we use principles of control theory 12,13 to demonstrate systematic control of place fields by pure optic flow information in freely moving animals by using a neurally closed-loop virtual reality system that adjusts optic flow speed as a function of real-time decoding of the hippocampal spatial map. Using this “cognitive clamp”, we show that we can not only bring the updating of the map under control of the optic flow cues but we can also elicit recalibration of path integration. This finding demonstrates that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".