TY - GEN
T1 - Scalable biologically inspired neural networks with spike time based learning
AU - Long, Lyle N.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=53849147548&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=53849147548&partnerID=8YFLogxK
U2 - 10.1109/LAB-RS.2008.24
DO - 10.1109/LAB-RS.2008.24
M3 - Conference contribution
AN - SCOPUS:53849147548
SN - 9780769532721
T3 - Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008
SP - 29
EP - 34
BT - Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008
T2 - 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008
Y2 - 6 August 2008 through 8 August 2008
ER -