Synergy between short-term and long-term plasticity explains direction-selectivity in visual cortex
Bibliographic record
Abstract
In this study, we examine whether short-term plasticity (STP) mediates learning rules governing long-term synaptic plasticity (LTSP). More specifically, we examine how the initial vesicle release probability can mediate long-term changes in synaptic strength. Given the importance of calcium-dependent modulation of synaptic transmission, as well as the temporal information to cortical computation, we examine whether STP can set the initial condition in modulating network connectivity strength and stability via spike-timing-dependent plasticity (STDP). Taking as a starting point the well-established Tsodyks-Markram (TM) rule for STP, we implement a model of two interconnected units receiving a train of incoming spikes first mediated by a mechanism of presynaptic STP. Extending the TM model, we then implement a mechanism of postsynaptic LTSP. By treating the two mechanisms synergistically, we manipulate the initial vesicle release probability of presynaptic STP and find that this process modulates long-term depression-mediated weight convergence, thus mediating the activity of postsynaptic responses. Furthermore, we show that an interaction between STP and LTSP jointly mediate neocortical synapses in explaining direction selectivity. Overall, results suggest that calcium-dependent modulation of synaptic strength mediates important consequences of neocortical response properties.
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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 | 0.000 | 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".