TY - GEN
T1 - Hebbian learning with winner take all for spiking neural networks
AU - Gupta, Ankur
AU - Long, Lyle N.
PY - 2009
Y1 - 2009
N2 - Learning methods for spiking neural networks are not as well developed as the traditional rate based networks, which widely use the back-propagation learning algorithm. We propose and implement an efficient Hebbian learning method with homeostasis for a network of spiking neurons. Similar to STDP, timing between spikes is used for synaptic modification. Homeostasis ensures that the synaptic weights are bounded and the learning is stable. The winner take all mechanism is also implemented to promote competitive learning among output neurons. We have implemented this method in a C++ object oriented code (called CSpike). We have tested the code on four images of Gabor filters and found bell-shaped tuning curves using 36 test set images of Gabor filters in different orientations. These bell-shapes curves are similar to those experimentally observed in the VI and MT/V5 area of the mammalian brain.
AB - Learning methods for spiking neural networks are not as well developed as the traditional rate based networks, which widely use the back-propagation learning algorithm. We propose and implement an efficient Hebbian learning method with homeostasis for a network of spiking neurons. Similar to STDP, timing between spikes is used for synaptic modification. Homeostasis ensures that the synaptic weights are bounded and the learning is stable. The winner take all mechanism is also implemented to promote competitive learning among output neurons. We have implemented this method in a C++ object oriented code (called CSpike). We have tested the code on four images of Gabor filters and found bell-shaped tuning curves using 36 test set images of Gabor filters in different orientations. These bell-shapes curves are similar to those experimentally observed in the VI and MT/V5 area of the mammalian brain.
UR - http://www.scopus.com/inward/record.url?scp=70449421908&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449421908&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2009.5178751
DO - 10.1109/IJCNN.2009.5178751
M3 - Conference contribution
AN - SCOPUS:70449421908
SN - 9781424435531
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 1054
EP - 1060
BT - 2009 International Joint Conference on Neural Networks, IJCNN 2009
T2 - 2009 International Joint Conference on Neural Networks, IJCNN 2009
Y2 - 14 June 2009 through 19 June 2009
ER -