This serves as a landing page for sharing code from our papers

Spiking Motion Estimation 

Code (github link)   for implementing the delay-based spiking neural network model for visual motion estimation described in the papers:
  • Orchard, G.; and Etienne-Cummings, R. “Bioinspired Visual Motion Estimation” Proceedings of the IEEE, vol. 102, no. 10, pp. 1520–1536, Oct. 2014. open access arXiv link
  • Orchard, G.; Benosman, R.; Etienne-Cummings, R.; and Thakor, N. "A Spiking Neural Network Architecture for Visual Motion Estimation,” IEEE Biomedical Circuits and Systems, Rotterdam, Holland, Nov 2013.
FPGA code (github link) for implementing the model in FPGA. Described in the paper:
  • Tun Aung, M.; Teo, R.; and Orchard, G.; “Event-based Plane-fitting Optical Flow for Dynamic Vision Sensors in FPGA” IEEE Int. Symp. Circuits Syst., 2018

Python event-based vision code

Some VERY preliminary Python code (github link) for reading, manipulating, and visualizing AER data, including the N-MNIST and N-Caltech101 datasets

DVS calibration

Matlab code (github link)  to help with calibrating a DVS sensor. Relies heavily on the free Caltech Camera Calibration toolbox


Code (github link) for a simple spiking neural network for recognition based on the canonical frame-based HMAX model described in the paper:
  • Orchard, G.; Meyer, C.; Etienne-Cummings, R.; Posch, C.; Thakor, N.; and Benosman, R., "HFIRST: A Temporal Approach to Object Recognition," Pattern Analysis and Machine Intelligence, IEEE Transactions on vol.37, no.10, pp.2028-2040, Oct. 2015 (open access arXiv link)

Matlab AER functions

Some basic functions for filtering and displaying AER vision data, as well as making videos (github link).