Differential encoding in a neural network includes predicting an activation value for a neuron in the neural network based on at least one previous activation value for the neuron. The encoding further includes encoding a value based on a difference between the predicted activation value and an actual activation value for the neuron in the neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of performing differential encoding in a neural network, comprising: predicting an activation value for a neuron in the neural network based at least in part on at least one previous activation value for the neuron; and encoding a value based at least in part on a difference between the predicted activation value and an activation value for the neuron in the neural network. 2. The method of claim 1, further comprising sending the encoded value between layers of the neural network. 3. The method of claim 2, in which the sent encoded value is at least one of the difference between the predicted activation value and the activation value and a thresholded difference between the predicted activation value and the activation value. 4. The method of claim 3, in which the sent encoded value is selected based at least in part on a number of bits of the encoded value. 5. The method of claim 1, in which the activation value is based at least in part on a nonlinear function. 6. The method of claim 1, in which predicting the activation value is performed based at least in part on receipt of an input. 7. The method of claim 1, further comprising encoding the value based at least in part on a bit width of the value. 8. The method of claim 1, in which the encoding is performed based at least in part on a neural network output based trigger. 9. The method of claim 1, in which the encoding is performed intermittently. 10. The method of claim 1, in which the encoding is delayed with respect to an input to the neural network. 11. The method of claim 1, in which the encoding is further based at least in part on an output of the neural network. 12. The method of claim 1, in which the at least one previous activation value comprises an input history when an input-output relationship is deterministic. 13. The method of claim 1, in which the at least one previous activation value comprises an input history and an output history when an input-output relationship is stochastic. 14. The method of claim 1, further comprising computing the predicted activation value based at least in part on a predicted input value. 15. The method of claim 14, further comprising computing an actual input value by combining the encoded value with the predicted input value. 16. The method of claim 14, in which computing the predicted input value and the predicted activation value comprises using a linear combination of a plurality of previous input values and a plurality of previous activation values for the neuron. 17. The method of claim 1, in which the predicted activation value for the neuron is based at least in part on a state for the neuron and an input to the neuron. 18. The method of claim 17, in which the state for the neuron is updated based on at least one of a previous state, an input value, an output value, a predicted activation value, and a targeted activation value. 19. The method of claim 17, in which the state for the neuron comprises at least one of an input history, an output history, a predicted activation value history, and a targeted activation value history. 20. The method of claim 19, in which the predicting is based at least in part on a state of another neuron. 21. The method of claim 1, in which the predicted activation value is based at least in part on a linear combination of a plurality of previous actual activation values or a linear combination of previous input values. 22. The method of claim 1, in which predicting the activation value comprises using an additional value provided to the neuron. 23. The method of claim 22, further comprising computing the additional value based at least in part on image motion estimation. 24. The method of claim 22, in which the additional value comprises a feedback signal from another neuron. 25. An apparatus for performing differential encoding in a neural network, comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured: to predict an activation value for a neuron in the neural network based at least in part on at least one previous activation value for the neuron; and to encode a value based at least in part on a difference between the predicted activation value and an activation value for the neuron in the neural network. 26. An apparatus for performing differential encoding in a spiking neural network, comprising: means for predicting an activation value for a neuron in the neural network based at least in part on at least one previous activation value for the neuron; and means for encoding a value based at least in part on a difference between the predicted activation value and an activation value for the neuron in the neural network. 27. A computer program product for performing differential encoding in a spiking neural network, comprising: a non-transitory computer readable medium having encoded thereon program code, the program code comprising: program code to predict an activation value for a neuron in the neural network based at least in part on at least one previous activation value for the neuron; and program code to encode a value based at least in part on a difference between the predicted activation value and an activation value for the neuron in the neural network.