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🤗 Models on Hugging Face 

Introducing TravNet

TravNet is a suite of functions to fully automate the seperation of neurons from noise. It uses preprocessing to take a binary data file and convert it to a waveblob (waveforms and their corresponding principal components). The waveblobs are then sorted using a pretrained convolutional neural network, trained on human sorted batches. The output is a file containing the waveforms corresponding cluster ID (neuron template), and timestamp.

Quick Start

You can follow the steps below to quickly get up and running with TravNet models.

  1. Create a conda env

  2. In the top-level directory run:

    pip install -e .
  3. To download model weights and sample data:

    python3 download_samples.py
  4. Once the model weights and data are available you can run the model using the command below:

    python3 example_sorter.py

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