Dynamical properties of spiking neural networks with small world topologies
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Bibliographic record
Abstract
Spiking neural networks can exhibit complex firing regimes whose characteristics are influenced by network topology. This paper is part of an investigation into the dynamical properties of spiking neural networks generated with small world topologies in comparison to those generated with Erdos-Renyi random graphs. Specifically, the parameters for small world and random graph network topology generation are tested empirically to find values which give rise to stable (fixed or periodic) vs. unstable or dissipative firing patterns. Similar to Erdos-Renyi random graph topologies, a critical threshold was found where the parameters of small world network generation lead to stable rather than dissipative patterns. Optimal parameters are identified for both small world and Erdos-Renyi random graph topologies which allow for stable firing patterns with minimal synapses. These results suggest questions that will form the basis for further research into the effects of topology class on firing dynamics of spiking neural networks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it