Biologically-inspired spiking neural networks with Hebbian learning for vision processing

Lyle N. Long, Ankur Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

This paper describes our recent efforts to develop biologically-inspired spiking neural network software (called JSpike) for vision processing. The ultimate goal is object recognition with both scale and translational invariance. This paper describes the initial software development effort, including code performance and memory requirement results. The software includes the neural network, image capture code, and graphical display programs. All the software is written in Java. The CPU time requirements for very large networks scale with the number of synapses, but even on a laptop computer billions of synapses can be simulated. While our initial application is image processing, the software is written to be very general and usable for processing other sensor data and for data fusion.

Original languageEnglish (US)
Title of host publication46th AIAA Aerospace Sciences Meeting and Exhibit
StatePublished - Dec 1 2008
Event46th AIAA Aerospace Sciences Meeting and Exhibit - Reno, NV, United States
Duration: Jan 7 2008Jan 10 2008

Publication series

Name46th AIAA Aerospace Sciences Meeting and Exhibit

Other

Other46th AIAA Aerospace Sciences Meeting and Exhibit
CountryUnited States
CityReno, NV
Period1/7/081/10/08

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

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    Long, L. N., & Gupta, A. (2008). Biologically-inspired spiking neural networks with Hebbian learning for vision processing. In 46th AIAA Aerospace Sciences Meeting and Exhibit [2008-0885] (46th AIAA Aerospace Sciences Meeting and Exhibit).