Dynamically Interacting Processes Underlie Synaptic Plasticity in a Feedback Pathway
Bibliographic record
Abstract
Descending feedback is a common feature of sensory systems. Characterizing synaptic plasticity in feedback inputs is essential for delineating the role of feedback in sensory processing. In this study, we demonstrate that multiple interacting processes underlie the dynamics of synaptic potentiation in one such sensory feedback pathway. We use field recording and modeling to investigate the interaction between the transient high-magnitude potentiation (200-300%) elicited during tetanic stimulation of the feedback pathway and the lower magnitude posttetanic potentiation (PTP; ~30%) that slowly decays on cessation of the tetanus. The amplitude of the observed transient potentiation is graded with stimulus frequency. In contrast, the induction of PTP has a stimulus frequency threshold between 1 and 5 Hz, and its amplitude is independent of stimulus frequency. We suggest that the threshold for PTP induction may be linked to a minimum level of sustained potentiation (MSP) during repetitive trains of stimuli. We have developed a novel model that describes the interaction between the transient plasticity observed during train stimulation and the generation of PTP. The model combines a multiplicative, facilitation-depression-type (FD) model that describes the transient plasticity, with an enzymatic network that describes the dynamics of PTP. The model links transient plasticity to PTP through an input term that reflects MSP. The stratum fibrosum-pyramidal cell (StF-PC) synapse investigated in this study is the terminus of a feedback pathway to the electrosensory lateral line lobe (ELL) of a weakly electric gymnotiform fish. Dynamic plasticity at the StF-PC synapse may contribute to the putative role of this feedback pathway as a sensory searchlight.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".