A computer-based method includes receiving an input signal at a neuron in a computer-based neural network that includes a plurality of neuron layers, applying a first non-linear transform to the input signal at the neuron to produce a plain signal, and calculating a weighted sum of a first component of the input signal and the plain signal at the neuron. In a typical implementation, the first non-linear transform is a function of the first component of the input signal and at least a second component of the input signal.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-based method comprising: receiving an input signal at a neuron in a computer-based neural network that comprises a plurality of neuron layers; applying a first non-linear transform to the input signal at the neuron to produce a plain signal; and calculating a weighted sum of a first component of the input signal and the plain signal at the neuron. 2. The method of claim 1, further comprising: applying a second non-linear transform to the input signal at a first gate in the neuron to produce a transform signal; and applying a third non-linear transform to the input signal at a second gate in the neuron to produce a carry signal. 3. The method of claim 2, wherein the first, second and third non-linear transforms are functions of the first component of the input signal and at least a second component of the input signal. 4. The method of claim 2, wherein the transform signal and the carry signal determine how the plain signal and the first component of the input signal are respectively weighted in calculating the weighted sum of the first component of the input signal and the plain signal at the neuron. 5. The method of claim 2, wherein the transform signal and the carry signal each have a value from zero to one, but the carry signal is not fixed at zero. 6. The method of claim 2 wherein calculating the weighted sum of the first component of the input signal and the plain signal comprises: calculating, with at least one computer-based multiplier, a product of the plain signal and the transform signal to produce a weighted plain signal; calculating, with the at least one computer-based multiplier, a product of the first component of the input signal and the carry signal to produce a weighted input signal component; and adding the weighted plain signal to the weighted input signal component to produce the weighted sum. 7. The method of claim 2, wherein each of the first, second and third non-linear transforms is a function of the input signal and associated weights. 8. The method of claim 1, wherein the neuron is in a first layer of the computer-based neural network, the method further comprising: outputting the weighted sum as an output signal to one or more other neurons in a second layer of the computer-based neural network. 9. The method of claim 1, wherein an input to each layer of the computer-based neural network is a vector and the output signal from each of the neurons is a scalar. 10. The method of claim 2, further comprising producing a network output signal at an output of the computer-based neural network. 11. The method of claim 10, wherein the computer-based neural network is configured to learn, and wherein the learning leads to a reduction in error associated with the network output signal, and wherein the learning comprises adjusting one or more parameters associated with one or more of the first, second or third non-linear transforms at the neuron. 12. The method of claim 11, wherein adjusting the one or more parameters associated with the second non-linear transform at the neuron, and adjusting the one or more parameters associated with the third non-linear transform at the neuron changes weighting associated with the plain signal and the input signal, respectively, to calculate the weighted sum. 13. The method of claim 1, wherein an input to the computer-based neural network represents an object at a single point in time. 14. The method of claim 1, wherein the neuron is among a plurality of neurons in a first of the plurality of neuron layers and wherein the neuron is configured to communicate with all of the neurons in at least a second one of the plurality of neuron layers. 15. The method of claim 1, wherein the computer-based neural network is a deep neural network having 20 layers of neurons or more. 16. A computer-based neural network comprising: a plurality of layers of neurons, wherein each layer of neurons comprises a plurality of neurons, and wherein one or more of the neurons comprises: an input to receive an input signal; a means for applying a first non-linear transform to the input signal to produce a plain signal; a means for calculating a weighted sum of a first component of the input signal and the plain signal; and an output to send the calculated weighted sum out of the neuron. 17. The computer-based neural network of claim 16, further comprising: a first gate configured to apply a second non-linear transform to the input signal to produce a transform signal; and a second gate configured to apply a third non-linear transform to the input signal to produce a carry signal. 18. The computer-based neural network of claim 17, wherein each of the transform signal and the carry signal has a value from zero to one. 19. The computer-based neural network of claim 16, wherein the means for applying the first non-linear transform to the input signal comprises a computer-based processing device configured to apply the first non-linear transform. 20. The computer-based neural network of claim 16, wherein the means for calculating the weighted sum of the first component of the input signal and the plain signal comprises a computer-based processing device embodying: at least one multiplier module configured to: calculate a product of the plain signal and the transform signal to produce a weighted plain signal; and calculate a product of the first component of the input signal and the carry signal to produce a weighted input signal component; and an adder module configured to add the weighted plain signal to the weighted input signal component to produce the weighted sum. 21. The computer-based neural network of claim 16, further comprising: a computer-based memory configured to store parameters associated with each of the first, second and third non-linear transforms associated with all of the neurons in the neural network, wherein each of the first, second and third non-linear transforms is a function of the input signal and the associated parameters. 22. The computer-based neural network of claim 16, wherein each neuron in each of the neuron layers is configured to communicate with all of the neurons in at least one other of the neuron layers. 23. The computer-based neural network of claim 22, wherein a neuron in a first layer of the computer-based neural network is configured to output the weighted sum as an output signal on the output of the neuron to a plurality of other neurons in a second layer of the computer-based neural network. 24. The computer-based neural network of claim 16, wherein an input to each layer in the plurality of layers of neurons is a vector, the weighted sum out of each of the neurons is a scalar and an output of the computer-based neural network is a vector. 25. The computer-based neural network of claim 16, further configured to produce a network output signal at an output of the computer-based neural network, wherein the computer-based neural network is configured to learn, such that the learning leads to a reduction in error associated with the network output signal, and wherein the learning comprises adjusting one or more parameters associated with the first non-linear transform, and/or the second non-linear transform, and/or the third non-linear transform, at each of one or more of the neurons. 26. The computer-based neural network of claim 25, wherein adjusting the one or more parameters associated with the second non-linear transform at the one or more of the neurons, and adjusting the one or more parameters associated with the third non-linear transform at the one or more of the neurons changes weighting associated with the plain signal and the first component of the input signal, respectively, to calculate the weighted sum. 27. The computer-based neural network of claim 16, wherein an input to the computer-based neural network represents an object at a single point in time. 28. The computer-based neural network of claim 16, wherein the computer-based neural network is a deep neural network having 20 layers of neurons or more. 29. In a computer-based neural network that comprises: a plurality of layers of neurons, wherein each layer of neurons comprises a plurality of neurons, and one or more of the neurons comprises: a means for applying a first non-linear transform to an input signal to produce a plain signal, a first gate configured to apply a second non-linear transform to the input signal to produce a transform signal, and a second gate configured to apply a third non-linear transform to the input signal to produce a carry signal, and a means for calculating a weighted sum of a first component of the input signal and the plain signal, a method of training the computer-based neural network, the method comprising: initializing parameters associated with each of the first, second and/or third of the non-linear transforms for each of a plurality of the neurons in the computer-based neural network; analyzing an input by passing an associated input signal through the computer-based neural network in a forward direction to produce an output signal at an output of the computer-based neural network; propagating an error associated with the output signal back through the computer-based neural network; and determining whether to adjust any parameters associated with the computer-based neural network. 30. A non-transitory, computer-readable medium that stores instructions executable by a processor to perform the steps comprising: receiving an input signal at a neuron in a computer-based neural network that comprises a plurality of neuron layers; applying a first non-linear transform to the input signal in the neuron to produce a plain signal; and calculating a weighted sum of a first component of the input signal and the plain signal at the neuron. 31. The non-transitory, computer-readable medium of claim 30 that stores further instructions executable by a processor to perform the steps comprising: applying a second non-linear transform to the input signal at a first gate in the neuron to produce a transform signal; and applying a third non-linear transform to the input signal at a second gate in the neuron to produce a carry signal. 32. The non-transitory, computer-readable medium of claim 31, wherein the transform signal and the carry signal determine how the plain signal and the input signal are respectively weighted in calculating the weighted sum of the first one of the input signals and the plain signal at the neuron, and wherein the transform signal has a value from zero to one, and wherein the carry signal has a value from zero to one. 33. The non-transitory, computer-readable medium of claim 30, wherein calculating the weighted sum of the first component of the input signal and the plain signal at the neuron comprises: calculating, with at least one computer-based multiplier, a product of the plain signal and the transform signal to produce a weighted plain signal; calculating, with the at least one computer-based multiplier, a product of the first component of the input signal and the carry signal to produce a weighted input signal component; and adding, with a computer-based adder, the weighted plain signal to the weighted input signal component, to produce the weighted sum.