Welcome to BehaveNet’s documentation!

BehaveNet is a probabilistic framework for the analysis of behavioral video and neural activity. This framework provides tools for compression, segmentation, generation, and decoding of behavioral videos. Please see the original paper and the follow-up paper on semi-supervised autoencoders for additional details.

Contents:

• Installation
  • Environment setup
  • Package installation
  • Set user paths
• User guide
  • Introduction
  • Autoencoders
  • Conditional autoencoders
  • Multi-session PS-VAE
  • ARHMMs
  • Decoders
  • Bayesian decoder
• Advanced user guide
  • Slurm job submission
  • Loading a trained model
  • Training a model with multiple datasets
  • PS-VAE hyperparameter search guide
  • MSPS-VAE hyperparameter search guide
• BehaveNet data structure
  • Introduction
  • Identifying subsets of neurons
  • Including labels for ARHMMs and conditional autoencoders
• Hyperparameter glossary
  • Data
  • Computational resources
  • Training
  • Models

BehaveNet API

• behavenet.data
• behavenet.fitting
• behavenet.models
• behavenet.plotting

Indices and tables

• Index

• Module Index

• Search Page