Self-organization and the brain
The handbook of brain theory and neural networks
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Dynamics and Topographic Organization of Recursive Self-Organizing Maps
Neural Computation
On non-markovian topographic organization of receptive fields in recursive self-organizing map
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Recursive self-organizing map as a contractive iterative function system
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Strong systematicity in sentence processing by an echo state network
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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As potential candidates for explaining human cognition, connectionist models of sentence processing must demonstrate their ability to behave systematically, generalizing from a small training set. It has recently been shown that simple recurrent networks and, to a greater extent, echo-state networks possess some ability to generalize in artificial language learning tasks. We investigate this capacity for a recently introduced model that consists of separately trained modules: a recursive self-organizing module for learning temporal context representations and a feedforward two-layer perceptron module for next-word prediction. We show that the performance of this architecture is comparable with echo-state networks. Taken together, these results weaken the criticism of connectionist approaches, showing that various general recursive connectionist architectures share the potential of behaving systematically.