Chavlis_Poirazi_2024

Dendritic Artificial Neural Networks (dANNs) with Receptive Fields (RFs)

These codes replicate: Chavlis, S., & Poirazi, P (2024). Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning. arXiv:2404.03708v1

To replicate the Figures of the manuscript, you need to install the Anaconda environment (see here)

1. Download the executable file and install it. Then, create a new environment from a terminal upon activation of anaconda (i.e., conda activate)
2. conda env create -f environment.yml
3. conda activate dann

You can run the files .py with python figure_2.py, for example, or train the model using the sh files.

GPU support

You need to install NVIDIA driver, CUDA 12.2 and then install tensorflow, pytorch and jax with cuda compatibility.

You can find your CUDA version with the command: nvcc --version

Tensorflow (https://www.tensorflow.org/install)

python3 -m pip install tensorflow[and-cuda]

and verify the installation: python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Pytorch (https://pytorch.org/get-started/locally/)

pip3 install torch torchvision torchaudio

and verify the installation: python3 -c "import torch; print(torch.cuda.is_available())"

Jax (https://jax.readthedocs.io/en/latest/installation.html)

pip install -U "jax[cuda12]"

and verify the installation: python3 -c "from jax.lib import xla_bridge; print(xla_bridge.get_backend().platform)"

CUML installation

pip install --extra-index-url=https://pypi.nvidia.com cuml-cu12

Extract the data

python unzip_data.py