Elucidating memory in the brain via single‐cell transcriptomics
Bibliographic record
Abstract
Elucidating the neural mechanisms of memory in the brain is a central goal of neuroscience. Here, we discuss modern-day transcriptomics methodologies, and how they are well-poised to revolutionize our insight into memory mechanisms at unprecedented resolution and throughput. Focusing on the hippocampus and amygdala, two regions extensively examined in memory research, we show how single-cell transcriptomics technologies have been leveraged to understand the naïve state of these brain regions. Building upon this foundation, we show that these technologies can be applied to single-trial learning paradigms to comprehensively identify molecules and cells that participate in the encoding and retrieval of memory. Transcriptomics also provides an opportunity to understand the cell-type organization of the human hippocampus and amygdala, and due to conservation of these brain regions between humans and rodents, to infer behavioral and causal contributions in the human brain by leveraging rodent cell-type homologies and interventions. Ultimately, such transcriptomic technologies are poised to usher in a qualitatively novel understanding of memory in the brain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".