An application of the principle of maximum information preservation to linear systems
Advances in neural information processing systems 1
Adaptation and decorrelation in the cortex
The computing neuron
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The cascade-correlation learning architecture
Advances in neural information processing systems 2
What does the retina know about natural scenes?
Neural Computation
Understanding retinal color coding from first principles
Neural Computation
Redundancy reduction as a strategy for unsupervised learning
Neural Computation
Convergent algorithm for sensory receptive field development
Neural Computation
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Towards a theory of early visual processing
Neural Computation
Neural Computation
Neural Computation
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Factorial learning, finding a statistically independent representation of a sensory “image”---a factorial code---is applied here to solve multilayer supervised learning problems that have traditionally required backpropagation. This lends support to Barlow's argument for factorial sensory processing, by demonstrating how it can solve actual pattern recognition problems. Two techniques for supervised factorial learning are explored, one of which gives a novel distributed solution requiring only positive examples. Also, a new nonlinear technique for factorial learning is introduced that uses neural networks based on almost reversible cellular automata. Due to the special functional connectivity of these networks---which resemble some biological microcircuits---learning requires only simple local algorithms. Also, supervised factorial learning is shown to be a viable alternative to backpropagation. One significant advantage is the existence of a measure for the performance of intermediate learning stages.