NDRAM: nonlinear dynamic recurrent associative memory for learning bipolar and nonbipolar correlated patterns.
This paper presents a new unsupervised attractor neural network, which, contrary to optimal linear associative memory models, is able to develop nonbipolar attractors as well as bipolar attractors. Moreover, the model is able to develop less spurious attractors and has a better recall performance under random noise than any other Hopfield type neural network. Those performances are obtained by a simple Hebbian/anti-Hebbian online learning rule that directly incorporates feedback from a specific nonlinear transmission rule. Several computer simulations show the model's distinguishing properties.
Chartier
Sylvain
S
Université du Québec à Montréal, Montréal QC, H3C3P8, Canada. chartier.sylvain@courrier.uqam.ca
Proulx
Robert
R
D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods; D016000:Cluster Analysis; D003198:Computer Simulation; D005246:Feedback; D008568:Memory; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D013223:Statistics as Topic