Exploring the Relationship Between Topology and Function in Evolved Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Understanding the relationship between structure and function in neural networks is essential to explaining their operation. Greater awareness of the link between topology and application could lead to wider adoption, particularly in mission-critical systems. Here, we examine and analyze the topology of very small, minimally sized neurocontrollers that have been evolved for an extended number of generations. Previously demonstrated Lamarckian-inherited neuromodulated evolutionary neurocontrollers are synthesized to operate a simulated vehicle pursuing a basic evader vehicle in the pursuit-evasion game. Both vehicles are subject to the effects of mass and drag. Constraints in the number of neurons and synapses are used to control network size. Additional objectives are added to the multiobjective optimization algorithm to encourage the selection of neural networks with the fewest neurons and synapses. It is shown that patterns emerge in the neuromodulatory neurons, in the direct connections between neurocontroller inputs and outputs, and that topologies similar to those used in classical control are evolved. Additionally, a neurocontroller constructed from the most commonly occurring neurons that successfully capture the evader is demonstrated.
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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 | 0.000 |
| 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) | 0.000 | 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