A method for selecting a reduced number of model neurons in a neural network includes generating a first sparse set of non-zero decoding vectors. Each of the decoding vector is associated with a synapse between a first neuron layer and a second neuron layer. The method further includes implementing the neural network only with selected model neurons in the first neuron layer associated with the non-zero decoding vectors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for selecting a reduced number of model neurons in a neural network, comprising: generating a first sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between a first neuron layer and a second neuron layer; and implementing the neural network only with selected model neurons in the first neuron layer associated with the non-zero decoding vectors. 2. The method of claim 1, in which the generating comprises performing a least squares optimization. 3. The method of claim 2, in which the least squares optimization comprises least absolute shrinkage and selection operator (LASSO) regularization. 4. The method of claim 2, further comprising selecting a regularization term to reduce a number of the selected neurons for a desired level of performance. 5. The method of claim 1, further comprising: updating the first neuron layer to include the selected model neurons and a new set of input synaptic weights for remaining model neurons; generating a second sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between the updated first neuron layer and the second neuron layer; and implementing the neural network only with a new set of selected model neurons in the updated first neuron layer associated with the non-zero decoding vectors. 6. The method of claim 5, further comprising: repeating the updating and generating a plurality of times to create a pooled set of updated first neuron layers; and implementing the neural network only with the pooled set of updated first neuron layers. 7. The method of claim 6, further comprising: generating a final sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between the pooled set of updated neuron layers and the second neuron layer; and implementing the neural network only with model neurons in the pooled set of updated neuron layers associated with the non-zero decoding vectors. 8. An apparatus for selecting a reduced number of model neurons in a neural network, comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured: to generate a first sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between a first neuron layer and a second neuron layer; and to implement the neural network only with selected model neurons in the first neuron layer associated with the non-zero decoding vectors. 9. The apparatus of claim 8, in which the at least one processor is further configured to generate the first sparse set of non-zero decoding vectors by performing a least squares optimization. 10. The apparatus of claim 9, in which the least squares optimization comprises least absolute shrinkage and selection operator (LASSO) regularization. 11. The apparatus of claim 9, in which the at least one processor is further configured to select a regularization term to reduce a number of the selected neurons for a desired level of performance. 12. The apparatus of claim 8, in which the at least one processor is further configured: to update the first neuron layer to include the selected model neurons and a new set of input synaptic weights for remaining model neurons; to generate a second sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between the updated first neuron layer and the second neuron layer; and to implement the neural network only with a new set of selected model neurons in the updated first neuron layer associated with the non-zero decoding vectors. 13. The apparatus of claim 12, in which the at least one processor is further configured: to repeat the updating and generating a plurality of times to create a pooled set of updated first neuron layers; and to implement the neural network only with the pooled set of updated first neuron layers. 14. The apparatus of claim 13, in which the at least one processor is further configured: to generate a final sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between the pooled set of updated neuron layers and the second neuron layer; and to implement the neural network only with model neurons in the pooled set of updated neuron layers associated with the non-zero decoding vectors. 15. An apparatus for selecting a reduced number of model neurons in a neural network, comprising: means for generating a first sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between a first neuron layer and a second neuron layer; and means for implementing the neural network only with selected model neurons in the first neuron layer associated with the non-zero decoding vectors. 16. The apparatus of claim 15, in which the generating comprises performing a least squares optimization. 17. The apparatus of claim 16, in which the least squares optimization comprises least absolute shrinkage and selection operator (LASSO) regularization. 18. The apparatus of claim 16, further comprising means for selecting a regularization term to reduce a number of the selected neurons for a desired level of performance. 19. The apparatus of claim 15, further comprising: means for updating the first neuron layer to include the selected model neurons and a new set of input synaptic weights for remaining model neurons; means for generating a second sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between the updated first neuron layer and the second neuron layer; and means for implementing the neural network only with a new set of selected model neurons in the updated first neuron layer associated with the non-zero decoding vectors. 20. The apparatus of claim 19, further comprising: means for repeating the updating and generating a plurality of times to create a pooled set of updated first neuron layers; and means for implementing the neural network only with the pooled set of updated first neuron layers. 21. The apparatus of claim 20, further comprising: means for generating a final sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between the pooled set of updated neuron layers and the second neuron layer; and means for implementing the neural network only with model neurons in the pooled set of updated neuron layers associated with the non-zero decoding vectors. 22. A computer program product for selecting a reduced number of model neurons in a neural network, comprising: a non-transitory computer readable medium having encoded thereon program code, the program code comprising: program code to generate a first sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between a first neuron layer and a second neuron layer; and program code to implement the neural network only with selected model neurons in the first neuron layer associated with the non-zero decoding vectors. 23. The computer program product of claim 22, further comprising program code to generate the first sparse set of non-zero decoding vectors by performing a least squares optimization. 24. The computer program product of claim 23, in which the least squares optimization comprises least absolute shrinkage and selection operator (LASSO) regularization. 25. The computer program product of claim 23, further comprising program code to select a regularization term to reduce a number of the selected neurons for a desired level of performance. 26. The computer program product of claim 22, further comprising: program code to update the first neuron layer to include the selected model neurons and a new set of input synaptic weights for remaining model neurons; program code to generate a second sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between the updated first neuron layer and the second neuron layer; and program code to implement the neural network only with a new set of selected model neurons in the updated first neuron layer associated with the non-zero decoding vectors. 27. The computer program product of claim 26, further comprising: program code to repeat the updating and generating a plurality of times to create a pooled set of updated first neuron layers; and program code to implement the neural network only with the pooled set of updated first neuron layers. 28. The computer program product of claim 27, further comprising: program code to generate a final sparse set of non-zero decoding vectors, each decoding vector being associated with a synapse between the pooled set of updated neuron layers and the second neuron layer; and program code to implement the neural network only with model neurons in the pooled set of updated neuron layers associated with the non-zero decoding vectors.