Formalizes how attractor networks emerge from the free energy principle applied to universal partitioning of random dynamical systems. Results in self-orthogonalizing attractor representations, biologically plausible multi-level Bayesian active inference. Use when: studying attractor dynamics in neural networks, free energy principle applications, Bayesian active inference models, biologically pla
no risky patterns detected
Type: Agent skill (SKILL.md) · Category: LLM & Model APIs · Source: https://github.com/hiyenwong/ai_collection/tree/HEAD/collection/skills/ai-ml/sel
| Capabilities (fewer powerful permissions = higher) | 50/50 |
| Maintenance | 21/35 |
| Documentation | 15/15 |
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