The Dynamic Multisensory Engram: Neural Circuitry Underlying Crossmodal Object Recognition in Rats Changes with the Nature of Object Experience
Bibliographic record
Abstract
Rats, humans, and monkeys demonstrate robust crossmodal object recognition (CMOR), identifying objects across sensory modalities. We have shown that rats' performance of a spontaneous tactile-to-visual CMOR task requires functional integration of perirhinal (PRh) and posterior parietal (PPC) cortices, which seemingly provide visual and tactile object feature processing, respectively. However, research with primates has suggested that PRh is sufficient for multisensory object representation. We tested this hypothesis in rats using a modification of the CMOR task in which multimodal preexposure to the to-be-remembered objects significantly facilitates performance. In the original CMOR task, with no preexposure, reversible lesions of PRh or PPC produced patterns of impairment consistent with modality-specific contributions. Conversely, in the CMOR task with preexposure, PPC lesions had no effect, whereas PRh involvement was robust, proving necessary for phases of the task that did not require PRh activity when rats did not have preexposure; this pattern was supported by results from c-fos imaging. We suggest that multimodal preexposure alters the circuitry responsible for object recognition, in this case obviating the need for PPC contributions and expanding PRh involvement, consistent with the polymodal nature of PRh connections and results from primates indicating a key role for PRh in multisensory object representation. These findings have significant implications for our understanding of multisensory information processing, suggesting that the nature of an individual's past experience with an object strongly determines the brain circuitry involved in representing that object's multisensory features in memory. SIGNIFICANCE STATEMENT: The ability to integrate information from multiple sensory modalities is crucial to the survival of organisms living in complex environments. Appropriate responses to behaviorally relevant objects are informed by integration of multisensory object features. We used crossmodal object recognition tasks in rats to study the neurobiological basis of multisensory object representation. When rats had no prior exposure to the to-be-remembered objects, the spontaneous ability to recognize objects across sensory modalities relied on functional interaction between multiple cortical regions. However, prior multisensory exploration of the task-relevant objects remapped cortical contributions, negating the involvement of one region and significantly expanding the role of another. This finding emphasizes the dynamic nature of cortical representation of objects in relation to past experience.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".