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Biologically-realistic recurrent convolutional neural networks

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Temporal Neural Networks

Run models in time.

Installation

git clone https:/github.com/dicarlolab/tnn
pip install -e tnn

(-e installs a developer version such that you can always update your code to the latest)

Usage

Look at tutorials. tutorials/alexnet_example.py demonstrates the basic unrolling API with AlexNet. tutorials/customcell_example.py shows how to pass a custom cell to a model, and add edges.

tnn/convrnn.py contains examples of standard ConvRNN cells in the literature. tnn/resnetrnn.py contains the Reciprocal Gated Cell implementation (see https://arxiv.org/abs/1807.00053 for details).

json contains a set of example graphs including 5 layer LSTM and Reciprocal Gated models. To use them with the customcell_example.py, set the global variables MODEL_JSON = 5L_imnet128_lstm345 and CUSTOM_CELL = tnn_ConvLSTMCell. You will also need to set the INPUT_LAYER and READOUT_LAYER to match the model JSON.

Contributors

  • Jonas Kubilius (MIT)
  • Daniel L.K. Yamins (Stanford)
  • Maryann Rui (Berkeley)
  • Harry Bleyan (MIT)
  • Aran Nayebi (Stanford)
  • Daniel Bear (Stanford)

License

MIT

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Biologically-realistic recurrent convolutional neural networks

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