A model of computation in neocortical architecture
Neural Networks - Special issue on organisation of computation in brain-like systems
A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
Minimizing Binding Errors Using Learned Conjunctive Features
Neural Computation
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Unsupervised learning of visual feature hierarchies
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Object detection and feature base learning with sparse convolutional neural networks
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
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We propose a cortically inspired hierarchical feedforward model for recognition and investigate a new method for learning optimal combination-coding cells in intermediate stages of the hierarchical network. The model architecture is characterized by weight-sharing, pooling, and Winner-Take-All nonlinearities. We show that an unsupervised sparse coding learning rule can be used to obtain a recognition architecture that is competitive with other more formally abstracted recognition approaches based on supervised learning. We evaluate the performance on object and face databases.