Connectionist learning procedures
Artificial Intelligence
Learning invariance from transformation sequences
Neural Computation
Neural Networks - Special issue: automatic target recognition
Unsupervised learning of temporal constancies by pyramidal-type neurons
MANNA '95 Proceedings of the first international conference on Mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Category learning through multimodality sensing
Neural Computation
Self-organization of shift-invariant receptive fields
Neural Networks
A learning rule for dynamic recruitment and decorrelation
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
How precise is neuronal synchronization?
Neural Computation
Learning Viewpoint Invariant Perceptual Representations from Cluttered Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning of position-invariant object representation across attention shifts
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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Neurons in mammalian cerebral cortex combine specific responses with respect to some stimulus features with invariant responses to other stimulus features. For example, in primary visual cortex, complex cells code for orientation of a contour but ignore its position to a certain degree. In higher areas, such as the inferotemporal cortex, translation-invariant, rotation-invariant, and even view point-invariant responses can be observed. Such properties are of obvious interest to artificial systems performing tasks like pattern recognition. It remains to be resolved how such response properties develop in biological systems. Here we present an unsupervised learning rule that addresses this problem. It is based on a neuron model with two sites of synaptic integration, allowing qualitatively different effects of input to basal and apical dendritic trees, respectively. Without supervision, the system learns to extract invariance properties using temporal or spatial continuity of stimuli. Furthermore, top-down information can be smoothly integrated in the same framework. Thus, this model lends a physiological implementation to approaches of unsupervised learning of invariant-response properties.