Broadband Coding with Dynamic Synapses
Bibliographic record
Abstract
Short-term synaptic plasticity (STP) can significantly alter the amplitudes of synaptic responses in ways that depend on presynaptic history. Thus, it is widely assumed that STP acts as a filter for specific patterns of presynaptic inputs, and as a result can play key roles in neuronal information processing. To evaluate this assumption and directly quantify the effects of STP on information transmission, we consider a population of independent synaptic inputs to a model neuron. We show using standard information theoretic approaches that the changes in synaptic response amplitude resulting from STP interact with the related effects on fluctuations in membrane conductance, such that information transmission is broadband (no frequency-dependent filtering occurs), regardless of whether synaptic depression or facilitation dominates. Interestingly, this broadband transmission is preserved in the postsynaptic spike train as long as the postsynaptic neuron's baseline firing rate is relatively high; in contrast, low baseline firing rates lead to STP-dependent effects. Thus, background inputs that control the firing state of a postsynaptic neuron can gate the effects of STP on information transmission.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".