A Topological Deep Learning Framework for Neural Spike Decoding

Edward C. Mitchell, Brittany Story, David Boothe, Piotr J. Franaszcuk, Vasileios Maroulas

We present simplicial convolutional neural networks (SCRNNs), which combine simplicial convolutions with backend recurrently connected layers. We showcase the network by decoding two types of brain cells: grid cells and head direction (HD) cells. The neural activity is first defined on a simplicial complex via a pre-processing procedure and then fed to the SCRNN for decoding. We also include the code for comparisons to a feed forward fully connected neural network (FFNN) and a recurrent neural network (RNN).

• Paper: arXiv:2212.05037

How to run

1. Decoding of each dataset is in properly named folder.

2. Open terminal at network_scripts folder

3. Run desired NN architecture

   • main.py: run either SCRNN or SCNN
   • main_mods.py (grid cell only): run either an SCRNN or SCNN where each grid cell module is treated independently in the pre-processing procedure
   • main_FFNN.py: run a FFNN
   • main_RNN.py: run a RNN