Scalable biologically inspired neural networks with spike time based learning

Lyle Norman Long

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

6 Scopus citations

Abstract

This paper describes the software and algorithmic issues involved in developing scalable large-scale biologically-inspired spiking neural networks. These neural networks are useful in object recognition and signal processing tasks, but will also be useful in simulations to help understand the human brain. The software is written using object oriented programming and is very general and usable for processing a wide range of sensor data and for data fusion.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008
Pages29-34
Number of pages6
DOIs
StatePublished - Oct 20 2008
Event2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008 - Edinburgh, Scotland, United Kingdom
Duration: Aug 6 2008Aug 8 2008

Publication series

NameProceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008

Other

Other2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008
CountryUnited Kingdom
CityEdinburgh, Scotland
Period8/6/088/8/08

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

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  • Cite this

    Long, L. N. (2008). Scalable biologically inspired neural networks with spike time based learning. In Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008 (pp. 29-34). [4599423] (Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008). https://doi.org/10.1109/LAB-RS.2008.24