A method of computation in a deep neural network includes discretizing input signals and computing a temporal difference of the discrete input signals to produce a discretized temporal difference. The method also includes applying weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix. The output of the weight matrix is temporally summed with a previous output of the weight matrix. An activation function is applied to the temporally summed output to create a next input signal to a next layer of the deep neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of computation in a deep neural network, comprising: discretizing a first input signal and a second input signal; computing a temporal difference of the discrete first input signal and the discrete second input signal to produce a discretized temporal difference; applying weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix; temporally summing the output of the weight matrix with a previous output of the weight matrix; and applying an activation function to the temporally summed output to create a next input signal to a next layer of the deep neural network. 2. The method of claim 1, further comprising: discretizing the next input signal; computing a second temporal difference of the discrete next input signal and a previous discrete input to the next layer to produce a discretized second temporal difference; applying weights of the next layer of the deep neural network to the discretized second temporal difference to create a second output of the weight matrix; temporally summing the second output of the weight matrix with the previous output of the weight matrix to produce a second summed output; and applying the activation function to the second summed output to create a subsequent input signal to a subsequent layer of the deep neural network. 3. The method of claim 1, in which the temporally summed output comprises a real vector corresponding to an approximation of extracted visual features. 4. The method of claim 1, in which the temporally summed output comprises a real vector corresponding to an approximation of classification results. 5. The method of claim 1, in which the discretizing comprises applying a herding process to the first input signal and the second input signal. 6. The method of claim 1, in which the discretizing comprises rounding the first input signal and the second input signal. 7. An apparatus for computation in a deep neural network, comprising: a memory; and at least one processor coupled to the memory, the at least one processor configured: to discretize a first input signal and a second input signal; to compute a temporal difference of the discrete first input signal and the discrete second input signal to produce a discretized temporal difference; to apply weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix; to temporally sum the output of the weight matrix with a previous output of the weight matrix; and to apply an activation function to the temporally summed output to create a next input signal to a next layer of the deep neural network. 8. The apparatus of claim 7, in which the at least one processor is further configured: to discretize the next input signal; to compute a second temporal difference of the discrete next input signal and a previous discrete input to the next layer to produce a discretized second temporal difference; to apply weights of the next layer of the deep neural network to the discretized second temporal difference to create a second output of the weight matrix; to temporally sum the second output of the weight matrix with the previous output of the weight matrix to produce a second summed output; and to apply the activation function to the second summed output to create a subsequent input signal to a subsequent layer of the deep neural network. 9. The apparatus of claim 7, in which the temporally summed output comprises a real vector corresponding to an approximation of extracted visual features. 10. The apparatus of claim 7, in which the temporally summed output comprises a real vector corresponding to an approximation of classification results. 11. The apparatus of claim 7, in which the at least one processor is further configured to discretize the input signal by applying a herding process to the input signal. 12. The apparatus of claim 7, in which the at least one processor is further configured to discretize the input signal by rounding the input signal. 13. An apparatus for computation in a deep neural network, comprising: means for discretizing a first input signal and a second input signal; means for computing a temporal difference of the discrete first input signal and the discrete second input signal to produce a discretized temporal difference; means for applying weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix; means for temporally summing the output of the weight matrix with a previous output of the weight matrix; and means for applying an activation function to the temporally summed output to create a next input signal to a next layer of the deep neural network. 14. The apparatus of claim 13, further comprising: means for discretizing the next input signal; means for computing a second temporal difference of the discrete next input signal and a previous discrete input to the next layer to produce a discretized second temporal difference; means for applying weights of the next layer of the deep neural network to the discretized second temporal difference to create a second output of the weight matrix; means for temporally summing the second output of the weight matrix with the previous output of the weight matrix to produce a second summed output; and means for applying the activation function to the second summed output to create a subsequent input signal to a subsequent layer of the deep neural network. 15. The apparatus of claim 13, in which the temporally summed output comprises a real vector corresponding to an approximation of extracted visual features. 16. The apparatus of claim 13, in which the temporally summed output comprises a real vector corresponding to an approximation of classification results. 17. The apparatus of claim 13, in which the means for discretizing discretizes the first input signal and the second input signal by applying a herding process to the first input signal and the second input signal. 18. The apparatus of claim 13, in which the means for discretizing discretizes the first input signal and the second input signal by rounding the first input signal and the second input signal. 19. A non-transitory computer readable medium having encoded thereon program code for computation in a deep neural network, the program code being executed by a processor and comprising: program code to discretize a first input signal and a second input signal; program code to compute a temporal difference of the discrete first input signal and the discrete second input signal to produce a discretized temporal difference; program code to apply weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix; program code to temporally sum the output of the weight matrix with a previous output of the weight matrix; and program code to apply an activation function to the temporally summed output to create a next input signal to a next layer of the deep neural network. 20. The non-transitory computer readable medium of claim 19, further comprising: program code to discretize the next input signal; program code to compute a second temporal difference of the discrete next input signal and a previous discrete input to the next layer to produce a discretized second temporal difference; program code to apply weights of the next layer of the deep neural network to the discretized second temporal difference to create a second output of the weight matrix; program code to temporally sum the second output of the weight matrix with the previous output of the weight matrix to produce a second summed output; and program code to apply the activation function to the second summed output to create a subsequent input signal to a subsequent layer of the deep neural network. 21. The non-transitory computer readable medium of claim 19, in which the temporally summed output comprises a real vector corresponding to an approximation of extracted visual features. 22. The non-transitory computer readable medium of claim 19, in which the temporally summed output comprises a real vector corresponding to an approximation of classification results. 23. The non-transitory computer readable medium of claim 19, further comprising program code to discretize the first input signal and the second input signal by applying a herding process to the first input signal and the second input signal. 24. The non-transitory computer readable medium of claim 19, further comprising program code to discretize the first input signal and the second input signal by rounding the first input signal and the second input signal.