Ca <sup>2+</sup> Plateau Potentials Reflect Cross-Theta Cortico-Hippocampal Input Dynamics and Acetylcholine for Rapid Formation of Efficient Place-Cell Code
Bibliographic record
Abstract
Summary A central tenet of Systems Neuroscience lies in an understanding of memory and behavior through learning rules, but synaptic plasticity has rarely been shown to create functional single-neuron code in a causal and biophysically rooted manner. Behavioral Time-Scale Synaptic Plasticity (BTSP), identified in vivo , holds a great potential for explaining instantaneous hippocampal selectivity emergence by long-term potentiation (LTP), yet the cellular and endogenous mechanisms are unknown, impeding broader conceptualization of this novel rule for its algorithmic, systems-level and theoretical implications. Here, we addressed this gap by in-vivo , ex-vivo , in-silico and computational approaches to seek neurophysiologically inspired protocols for synaptically evoking Ca 2+ plateau potentials and inducing potentiation in the CA1. We found induction of BTSP-LTP is best explained by a theta-oscillation-paced, gradually developed cellular state being supported with precisely timed weak ramping inputs. Remarkably, the previously presumed one-shot LTP for in-vivo place-field formation is possible under the influence of muscarinic activation. Through modeling, the notion of acetylcholine-gated BTSP gave rise to a computational advantage for low-interference continual learning. We further demonstrated that biophysics of Transient Receptor Potential (TRPM) and NMDA receptor (NMDAR) channels powerfully shapes the cross-theta dynamics underlying BTSP. These results which cover pre-, post-synaptic and neuromodulatory factors and their timing suggest fundamental principles for graded plateau potentials and hippocampal LTP induction. Overall, our work dissects cellular mechanisms potentially important for a prominent in-vivo hippocampal plasticity phenomenon, and offers a biological basis for framing BTSP as an input-dynamics-aware, neuromodulation-tuned synaptic algorithm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".