"What if a Tamagotchi had a neural network and could learn stuff?" — Gigazine

Dosidicus electronicus

🦑 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

• Part educational neuro tool, part sim game, part fever dream
• Build-your-own neural network - learn neuroscience by raising a squid that might develop irrational fears
• Custom simulation engine using Numpy - No Tensorflow or PyTorch
• Most AI is a black box; Dosidicus is transparent - every neuron is visible, stimulatable, understandable.
• Starts with 8 neurons — grows via neurogenesis and rewires using Hebbian learning.

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  ## Quick Start

git clone ...
cd Dosidicus
pip install -r requirements.txt
python main.py

Windows Download: see Releases page

Linux users can try the experimental bash setup: :

curl -sSL https://raw.githubusercontent.com/ViciousSquid/Dosidicus/2.6.1.2_LatestVersion/linux_setup.sh | bash

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Manifesto | Wiki | Changelog

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Myth & Mechanism

Dosidicus is a digital squid born with a randomly wired brain.

Feed him., stimulate neurons, watch him learn.

• He starts with 8 neurons.
• He rewires through Hebbian learning.
• He grows new structure via neurogenesis.
• He forms memories.
• He develops quirks.

Every squid is different. Every save file is a cognitive history.

Under the hood runs STRINg simulation engine:

• Built from scratch in NumPy
• No TensorFlow. No PyTorch.
• Fully visible neuron activations
• Structural growth over time
• Dual memory system
• Headless training mode
• Most AI is a black box: Dosidicus lets you see the mind forming.

Want the full conceptual philosophy behind Dosidicus? → Read the Cognitive Sandbox Manifesto

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🦑 Share Your Squid

No two squids are wired the same.

Early interactions permanently alter their structure. Tiny differences amplify. Habits form. Fears emerge. Personalities drift.

Your squid’s brain is a cognitive history — shaped by you.

So share it.

• Export save files and let others explore your squid’s neural structure.

• Post screenshots of strange activation patterns and unexpected growth.

• Show bizarre learned behaviors (Why is yours afraid of poop?)

• Compare cognitive histories and trace how experience shaped structure.

• Did yours grow 40 neurons?

• Did it develop a persistent avoidance loop?

• Did you accidentally create a neurotic reward spiral?

Every squid is an experiment.

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Project Overview

• ~80K lines, one developer, 28 months

• Dependencies:

  • Python ^3.9
  • PyQt5 ^5.15 (GUI framework)
  • numpy ^1.21 (neural network computations)
  • OPTIONAL onnxruntime or onnxruntime-directml (more info)

• Core Structure: Modular codebase in src/ including brain designer, decision engine, learning algorithms, personality traits, memory management, UI components, and interaction systems. Entry point via main.py.

Key Project Components

• Plugin System: Extensible architecture with built-in plugins for achievements (tracking milestones) and multiplayer (networked interactions).
• Save System: Persistent saves in saves/ for pet states, autosaves, and achievement logs.
• Headless Mode: Standalone training and simulation in headless/ for GUI-less operation, ideal for background training or server environments (experimental)
• Custom Brains: Library of pre-configured neural networks in custom_brains/ (e.g., "Plant-Seeker", "Insomniac") for quick behavior setup.
• Memory Management: Dual memory system (_memory/) with long-term and short-term storage for learning persistence.
• Examples and Tools: Example squids, configuration files (config.ini), and version tracking.

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A year ago I got a tattoo of this project to celebrate its first development milestone!

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