Title :
Scalable Biologically Inspired Neural Networks with Spike Time Based Learning
Author_Institution :
Pennsylvania State Univ., University Park, PA
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.
Keywords :
biology computing; brain; learning (artificial intelligence); neural nets; object recognition; object-oriented programming; sensor fusion; biologically inspired neural networks; biologically inspired spiking neural networks; data fusion; human brain; object oriented programming; object recognition; sensor data; signal processing tasks; spike time based learning; Biological neural networks; Biological system modeling; Biomedical signal processing; Brain modeling; Large-scale systems; Neural networks; Object oriented modeling; Object recognition; Signal processing algorithms; Software algorithms; hebbian; neural network; parallel; spiking; stdp;
Conference_Titel :
Learning and Adaptive Behaviors for Robotic Systems, 2008. LAB-RS '08. ECSIS Symposium on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-7695-3272-1
DOI :
10.1109/LAB-RS.2008.24
