Biological Connectivity Patterns as a Blueprint for Efficient Neural Architectures in Reinforcement Learning
Bibliographic record
Abstract
Artificial neural networks (ANNs) demonstrate remarkable performance across various domains, yet their computational demands remain a significant challenge. In contrast, biological neural networks achieve exceptional efficiency through sparse structured connectivity shaped by evolution, while maintaining high performance. This study investigates whether bio-inspired connectivity patterns can improve ANN design by balancing efficiency and performance. We systematically compare the performance of Neural Circuit Architectural Priors (NCAP), a neuro- inspired reinforcement learning model, with that of Multi-Layer Perceptrons (MLPs) compressed through pruning and knowledge distillation, in a swimming task. Our findings reveal that NCAP achieves comparable performance to larger, more complex MLPs with significantly fewer parameters. We also observe that sparse, but optimally connected small MLPs often outperform larger MLPs with suboptimal connectivity, underscoring the importance of connectivity in efficient learning. This work paves the way for the development of computationally efficient models that maintain high performance by integrating biological connectivity principles into ANN architectures.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".