TorchBrain documentation

TorchBrain is a library for writing training pipelines and deep learning models focused on neural data.

Its goal is to provide PyTorch-compatible interfaces like Dataset, Samplers, nn.Modules, and more to help you write your very own models and training scripts. To achieve efficient training, TorchBrain defines its own light-weight data format. It stores neural recordings temporally and is optimized for lazily loading arbitrary time-slices. Finally, TorchBrain also provides a collection of tools to pre-process existing datasets into this data format.

If you encounter any bugs or have feature requests, please submit them to our GitHub Issues page.

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• Install TorchBrain
• User Guide
  • The Data Format
    • Meet the Data Objects
    • Saving and Loading Data
    • Interval Operations
    • Domains
    • Regular Time Series with Gaps
  • Brainsets: Dataset Pre-Processing Tools
    • Using the brainsets CLI
    • Preparing a new Dataset
    • Creating a BrainsetPipeline
    • Creating an OpenNeuroPipeline
  • Training Pipeline Concepts
    • Data Sampling
  • Examples
    • A Minimal Training Setup on NLB Maze
    • Working with the Neuroprobe Benchmark

• CLI Reference

• API Reference
  • torch_brain.batching
  • torch_brain.data
  • torch_brain.datasets
  • torch_brain.models
  • torch_brain.nn
  • torch_brain.pipeline
  • torch_brain.pipeline.openneuro
  • torch_brain.samplers
  • torch_brain.transforms
  • torch_brain.utils
  • torch_brain.utils.bids
  • torch_brain.utils.dandi
  • torch_brain.utils.mne
  • torch_brain.utils.openneuro
  • torch_brain.utils.s3
  • torch_brain.utils.signal
  • torch_brain.utils.split
  • torch_brain.utils.stitcher