Probabilistic Spiking Neural Network Research Internship

References

Papers

• Probabilistic Spiking Neural Networks
• Expectation Maximization for PSSNs
• Variational Bayes Algorithm 4
• Variational Auto-Encoders Useful for understanding Partially Observed training methods

Resources

• Norse Pytorch SSN Library

Abstract

This repo contains code and results I obtained as part of an internship researching SNNs

• PSSN/ Julia PSSN implementation
• Norse-Pytorch/ Norse demo for comparison
• TestData.xlsx Training results and parameters. Useful for comparing alternative implementations
• PSSN_Presentation.pptx A presentations with some visuals describing obtained results and findings

Set Up

• Run the PSSN/DataEncoding.jl file to generate the encoded data
• Run Norse-Pytorch/norse-3layer.ipynb to train the norse demo, you can select Iris or MNIST dataset
• Run PSSN/Main.jl to train the julia model. PSSN/PSSN.jl contains the various funtions and structs to define and train a network

Next Steps

• Test PSSN implementation on more diverse training problems
• Fully and Partially Observed models can potentially be combined into a single function using EM
• Port to FluxML or Pytorch for potential performance gain and possibility to utilize back-propagation with the reparameterization trick