Oscillatory network coding of a global stimulus
Bibliographic record
Abstract
The pyramidal cells of weakly electric fish respond to environmental broadband electrical stimuli. They have recently been shown to exhibit oscillations in mean firing rate in response to global stimuli that affect the whole body simultaneously similar to communication stimuli for these animals. In contrast, for spatially localized stimuli such as those produced by prey, the firing rate simply fluctuates around a constant mean. This switch in coding strategy relies on delayed negative (inhibitory) feedback connections in the neural network. We first summarize these experimental findings, as well as our mathematical modeling of this effect using a globally-coupled delayed inhibitory network of leaky integrate-and-fire neurons (LIF's). Here we study the mechanism of the transition from oscillatory to non-oscillatory firing states in such networks. This is done using simulations of a simpler network of LIF's with current based Gaussian white noise stimuli, rather than conductance based bandlimited Gaussian stimuli. We focus on the effect of feedback gain, current bias, and stimulus intensity on the oscillation under global conditions, and see how the decrease of these parameters brings on a response characteristic of the local case. These simulations are performed for a fixed amount of individual synaptic noise to each cell. We also show how insights into these results can be obtained from the analysis of stimulus-induced oscillations in a simpler rate model description of this spatially-extended excitable system.
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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 | 0.001 |
| 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 | 0.000 |
| 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".