vtem

This package provides code to encode and decode videos with time encoding machines consisting of gabor or centre surround receptive fields followed by Integrate-and-fire neurons .It supports both the pseudoinverse algorithm and recurrent neural networks method for decoding.


Keywords
Time, Enoding, Decoding, Population, encoding, nonuniform-sampling, python, time-decoding, time-encoding, video-processing
License
BSD-3-Clause
Install
pip install vtem==0

Documentation

Video Time Encoding and Decoding Machines

Package Description

This package provides code to encode and decode videos with time encoding machines consisting of gabor or centre 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.

Authors & Acknowledgments

This software was written by Yiyin Zhou and packaged by Nikul Ukani, both currently at the Bionet Group [4] at Columbia University.

License

This software is licensed under the BSD License. See the included LICENSE file for more information.

Contact Information

Please direct all questions and comments pertaining to this software to

Nikul Ukani and Yiyin Zhou

[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
[4] http://www.bionet.ee.columbia.edu/