TY - GEN
T1 - A robust biologically plausible implementation of ICA-like learning
AU - Gerhard, Felipe
AU - Savin, Cristina
AU - Triesch, Jochen
PY - 2009
Y1 - 2009
N2 - We present a model that can perform ICA-like learning by simple, local, biologically plausible rules. By combining synaptic learning with homeostatic regulation of neuron properties and adaptive lateral inhibition, the neural network can robustly learn Gabor-like receptive fields from natural images. With spatially localized inhibitory connections, a topographic map can be achieved. Additionally, the network can solve the Földiák bars problem, a classical nonlinear ICA task.
AB - We present a model that can perform ICA-like learning by simple, local, biologically plausible rules. By combining synaptic learning with homeostatic regulation of neuron properties and adaptive lateral inhibition, the neural network can robustly learn Gabor-like receptive fields from natural images. With spatially localized inhibitory connections, a topographic map can be achieved. Additionally, the network can solve the Földiák bars problem, a classical nonlinear ICA task.
UR - http://www.scopus.com/inward/record.url?scp=84887012006&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887012006&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84887012006
SN - 2930307099
SN - 9782930307091
T3 - ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
SP - 147
EP - 152
BT - ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
T2 - 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009
Y2 - 22 April 2009 through 24 April 2009
ER -