Video Time Encoding and Decoding Machines

This package provides code to encode and decode videos with time encoding machines consisting of Gabor or center-surround receptive fields followed by Integrate-and-fire neurons [1] , [2] . It supports both the pseudoinverse algorithm, described in [1] , [2] and recurrent neural networks method, described in [3] for decoding.

Additional time encoding and decoding algorithms implemented in Python are available in the Time Encoding and Decoding (TED) Toolkit.

Contents

• Installation Instructions
  • Quick Installation
  • Obtaining the Latest Software
  • Installation Dependencies
  • Running the demos
  • Platform Support
  • Building and Installation
  • Getting Started
• Reference
  • vtem.vtem
  • vtem.vtdm
  • vtem.ensemble_encode
  • vtem.vrf
• Authors & Acknowledgments
• License
• Change Log
  • Release 0.1.1 - (June 7, 2013)

Index

• Index
• Search Page

[1]	(1, 2) Video Time Encoding Machines, Aurel A. Lazar and Eftychios A. Pnevmatikakis, IEEE Transactions on Neural Networks, Volume 22 , Number 3 , pp. 461-473 , March 2011

[2]	(1, 2) Encoding Natural Scenes with Neural Circuits with Random Thresholds, Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou, Vision Research, Volume 50, Number 22, pp. 2200-2212, October 2010, Special Issue on Mathematical Models of Visual Coding

[3]	Massively Parallel Neural Encoding and Decoding of Visual Stimuli, Aurel A. Lazar and Yiyin Zhou, Neural Networks, Volume 32, pp. 303-312, August 2012, Special Issue: IJCNN 2011