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"What if a Tamagotchi had a neural network and could learn stuff?" — Gigazine
🦑 A transparent cognitive sandbox disguised as a digital pet squid with a neural network you can see thinking
Micro neural engine for small autonomous agents that learn via Hebbian dynamics and grow new structure
New to Dosidicus? Start here to understand how to interact with your squid.
- Care Guide / Getting Started
- Personalities — How different squid types behave.
- Decoration Window — Managing the squid's environment.
- Vision System — Realistic foraging and food detection.
- Hebbian Learning — The algorithm behind 30-second learning cycles.
- Neurogenesis — How the squid creates new neurons based on environment.
- Decision Engine — Making choices based on hunger, sleep, and memory.
- Engine Overview — High-level system architecture.
- Data Flow Summary
- How Dosidicus differs from other systems - a unique form of artificial neurogenesis
- main.py — The main simulation loop.
- tamagotchi_logic.py — Core needs and health management.
- squid.py — The physical squid class.
- Memory System & memory_manager.py — Managing experiences.
Fine-tune the simulation and monitor the squid's neural activity.
- config.ini — Adjusting simulation parameters.
- Save File Format — Structure of persisted data.
- Plugin System — Extending the engine's capabilities.
- https://medium.com/@reutdayan1/hebbian-learning-biologically-plausible-alternative-to-backpropagation-6ee0a24deb00
- https://informatics.ed.ac.uk/sites/default/files/2024-03/Qiuye%20Zhang%20Lovelace%20Colloquium%20Poster.pdf
- https://en.wikipedia.org/wiki/Hebbian_theory
- https://www.youtube.com/watch?v=TvTQQO5yTa4
- https://pmc.ncbi.nlm.nih.gov/articles/PMC7781593/