Embodiments of the present invention provide a method for feature extraction comprising generating synaptic connectivity information for a neurosynaptic core circuit. The core circuit comprises one or more electronic neurons, one or more electronic axons, and an interconnect fabric including a plurality of synapse devices for interconnecting the neurons with the axons. The method further comprises initializing the interconnect fabric based on the synaptic connectivity information generated, and extracting a set of features from input received via the electronic axons. The set of features extracted comprises a set of features with reduced correlation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: selecting a first matrix and a second matrix; computing a product based on the first matrix and the second matrix; initializing a core circuit for feature extraction based on the product, wherein the core circuit comprises a plurality of synapses interconnecting a plurality of axons to a plurality of neurons; receiving, at the core circuit, input; and extracting, via the core circuit, a maximally uncorrelated set of features from the input. 2. The method of claim 1, wherein the first matrix is an orthogonal matrix, and the second matrix is a binary matrix. 3. The method of claim 2, wherein the computing comprises computing a Kronecker product between the first matrix and the second matrix. 4. The method of claim 2, wherein the binary matrix is a random binary matrix comprising entries that are independent and binomially distributed. 5. The method of claim 2, wherein the binary matrix is learned based on a distribution of input received by the core circuit. 6. The method of claim 1, wherein the initializing comprises: initializing synaptic weights for the plurality of synapses; and initializing effective synaptic strengths for the plurality of axons. 7. The method of claim 1, wherein the input comprises an image, and the set of features extracted includes at least one feature relating at least one of color, texture, and shape. 8. A system comprising a computer processor, a computer-readable hardware storage medium, and program code embodied with the computer-readable hardware storage medium for execution by the computer processor to implement a method comprising: selecting a first matrix and a second matrix; computing a product based on the first matrix and the second matrix; initializing a core circuit for feature extraction based on the product, wherein the core circuit comprises a plurality of synapses interconnecting a plurality of axons to a plurality of neurons; receiving, at the core circuit, input; and extracting, via the core circuit, a maximally uncorrelated set of features from the input. 9. The system of claim 8, wherein the first matrix is an orthogonal matrix, and the second matrix is a binary matrix. 10. The system of claim 9, wherein the computing comprises computing a Kronecker product between the first matrix and the second matrix. 11. The system of claim 9, wherein the binary matrix is a random binary matrix comprising entries that are independent and binomially distributed. 12. The system of claim 9, wherein the binary matrix is learned based on a distribution of input received by the core circuit. 13. The system of claim 8, wherein the initializing comprises: initializing synaptic weights for the plurality of synapses; and initializing effective synaptic strengths for the plurality of axons. 14. The system of claim 8, wherein the input comprises an image, and the set of features extracted includes at least one feature relating at least one of color, texture, and shape. 15. A computer program product comprising a computer-readable hardware storage medium having program code embodied therewith, the program code being executable by a computer to implement a method comprising: selecting a first matrix and a second matrix; computing a product based on the first matrix and the second matrix; initializing a core circuit for feature extraction based on the product, wherein the core circuit comprises a plurality of synapses interconnecting a plurality of axons to a plurality of neurons; receiving, at the core circuit, input; and extracting, via the core circuit, a maximally uncorrelated set of features from the input. 16. The computer program product of claim 15, wherein the first matrix is an orthogonal matrix, and the second matrix is a binary matrix. 17. The computer program product of claim 16, wherein the computing comprises computing a Kronecker product between the first matrix and the second matrix. 18. The computer program product of claim 16, wherein the binary matrix is a random binary matrix comprising entries that are independent and binomially distributed. 19. The computer program product of claim 16, wherein the binary matrix is learned based on a distribution of input received by the core circuit. 20. The computer program product of claim 15, wherein the initializing comprises: initializing synaptic weights for the plurality of synapses; and initializing effective synaptic strengths for the plurality of axons.