Embodiments of the invention provide a method comprising receiving a set of features extracted from input data, training a linear classifier based on the set of features extracted, and generating a first matrix using the linear classifier. The first matrix includes multiple dimensions. Each dimension includes multiple elements. Elements of a first dimension correspond to the set of features extracted. Elements of a second dimension correspond to a set of classification labels. The elements of the second dimension are arranged based on one or more synaptic weight arrangements. Each synaptic weight arrangement represents effective synaptic strengths for a classification label of the set of classification labels. The neurosynaptic core circuit is programmed with synaptic connectivity information based on the synaptic weight arrangements. The core circuit is configured to classify one or more objects of interest in the input data
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: receiving a set of features extracted from input data; training a linear classifier based on the set of features extracted; generating a first matrix using the linear classifier, wherein the first matrix includes multiple dimensions, wherein each dimension includes multiple elements, wherein elements of a first dimension correspond to the set of features extracted, and wherein elements of a second dimension correspond to a set of classification labels; arranging the elements of the second dimension based on one or more synaptic weight arrangements, wherein each synaptic weight arrangement represents effective synaptic strengths for a classification label of the set of classification labels; and programming a neurosynaptic core circuit with synaptic connectivity information based on the one or more synaptic weight arrangements, wherein the core circuit is configured to classify one or more objects of interest in the input data. 2. The method of claim 1, wherein the core circuit comprises one or more electronic neurons, one or more electronic axons, and a synaptic array including a plurality of electronic synapse devices for interconnecting the neurons with the axons. 3. The method of claim 1, wherein the input data comprises one of an image sequence, an audio sequence, and a video sequence. 4. The method of claim 2, wherein the programming step further comprises: setting synaptic weights for the synapse devices based on the one or more synaptic weight arrangements. 5. The method of claim 2, wherein: each element of the first dimension corresponds to a feature of the set of features extracted; and each element of the second dimension corresponds to a classification label of the set of classification labels. 6. The method of claim 5, wherein the arranging step further comprises: arranging each element of the second dimension into a synaptic weight arrangement representing effective synaptic strengths for a classification label corresponding to the element. 7. The method of claim 6, wherein: each element of the first dimension represents a row of the first matrix; each element of the second dimension represents a column of the first matrix; and the arranging step further comprises: dividing at least one column of the first matrix into multiple columns. 8. The method of claim 7, wherein each column of the first matrix is divided into multiple columns based on a Latin square. 9. The method of claim 8, further comprising: mapping the first matrix to the synaptic array of the core circuit to reduce spiking of the neurons of the core circuit. 10. The method of claim 2, wherein: each axon has a corresponding axon type; and each synaptic weight arrangement represents effective synaptic strengths for neurons assigned to a particular classification label for all possible axon types. 11. A neurosynaptic system, comprising: one or more classification units for assigning one or more classification labels to one or more features extracted from input data; wherein at least one classification unit comprises at least one neurosynaptic core circuit configured to classify one or more object of interests in the input data, and wherein the core circuit is programmed with synaptic connectivity information based on one or more synaptic weight arrangements. 12. The system of claim 11, wherein: the core circuit comprises one or more electronic neurons, one or more electronic axons, and a synaptic array including a plurality of electronic synapse devices for interconnecting the neurons with the axons. 13. The system of claim 12, wherein: a synaptic weight arrangement corresponds to a column of a first matrix generated by a linear classifier, wherein the linear classifier is trained based on a set of features extracted from the input data, wherein the first matrix includes multiple rows and multiple columns, wherein a row of the first matrix corresponds to a feature of the set of features extracted, and wherein a column of the first matrix corresponds to a classification label and is arranged into a corresponding synaptic weight arrangement representing effective synaptic strengths for the classification label. 14. The system of claim 11, wherein the input data comprises one of an image sequence, an audio sequence, and a video sequence. 15. The system of claim 13, wherein, for the core circuit, synaptic weights for at least one synapse device of the core circuit is set based on the one or more synaptic weight arrangements. 16. The system of claim 13, wherein a column of the first matrix are arranged into a synaptic weight arrangement by dividing the column into multiple columns. 17. The system of claim 16, wherein a column of the first matrix is divided into multiple columns based on a Latin square. 18. The system of claim 17, wherein the first matrix is mapped to the synaptic array of the core circuit to reduce spiking of the neurons of the core circuit. 19. The system of claim 13, wherein: each axon has a corresponding axon type; and each synaptic weight arrangement represents effective synaptic strengths for neurons assigned to a particular classification label for all possible axon types. 20. A computer program product for programming a neurosynaptic core circuit with synaptic connectivity information, the computer program product comprising a computer-readable storage medium having program code embodied therewith, the program code being executable by a computer to: receive a set of features extracted from input data; train a linear classifier based on the set of features extracted; generate a first matrix using the linear classifier, wherein the first matrix includes multiple dimensions, wherein each dimension includes multiple elements, wherein elements of a first dimension correspond to the set of features extracted, and wherein elements of a second dimension correspond to a set of classification labels; arrange the elements of the second dimension based on one or more synaptic weight arrangements, wherein each synaptic weight arrangement represents effective synaptic strengths for a classification label of the set of classification labels; and program a neurosynaptic core circuit with synaptic connectivity information based on the one or more synaptic weight arrangements, wherein the core circuit is configured to classify one or more objects of interest in the input data.