iBOTS
Larvaworld
Agent-based modeling and simulation platform for Drosophila larva behavior
Overview
A comprehensive Python package for modeling, simulating and analyzing insect larva behavior with a focus on Drosophila melanogaster. Provides tools for importing experimental datasets, combining behavior-based models in a modular Behavioral Architecture, visualizing simulations and generating diagrams and videos.
A Python-based platform for simulating and analyzing insect larva behavior, with a particular focus on Drosophila melanogaster fruit fly larvae. Larvaworld combines agent-based modeling, behavioral analysis, and neural circuit simulation in a unified framework.
Features
- Agent-Based Simulation: Model larval behavior with configurable agents and environments
- Behavioral Analysis: Tools for analyzing real and simulated larval tracking data
- Neural Modeling: Simulate neural circuits controlling locomotion and decision-making
- Flexible Framework: Extensible architecture for custom models and experiments
- Comprehensive Documentation: Detailed guides and API reference at larvaworld.readthedocs.io
Resources
- Documentation: larvaworld.readthedocs.io
- Source Code: github.com/nawrotlab/larvaworld
- PyPI Package: pypi.org/project/larvaworld
- Research Paper: bioRxiv preprint
Use Cases
- Study foraging strategies and navigation algorithms in larvae
- Validate behavioral models against experimental tracking data
- Investigate how neural circuits generate complex behaviors
- Compare different computational models of decision-making
- Teach computational neuroscience and behavioral modeling
Technical Details
Built with Python, LarvaWorld provides a modular platform for researchers studying insect behavior and neural control. The package integrates simulation, analysis, and visualization tools in a single framework, making it accessible for both computational and experimental researchers.