A method for implementing spiking neural network computations, the method including defining a dynamic node response function that exhibits spikes, where spikes are temporal nonlinearities for representing state over time; defining a static representation of said node response function; and using the static representation of the node response function to train a neural network. A system for implementing the method is also disclosed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for implementing spiking neural network computations, the method comprising: defining a dynamic node response function that exhibits spikes, where spikes are temporal nonlinearities for representing state over time; defining a static representation of said node response function; using the static representation of the node response function to train a neural network. 2. The method of claim 1, further comprising deriving neural connection weights from the step of using the static representation of the node response function to train the neural network; wherein the derived connection weights are used in a dynamically implemented spiking neural network to compute the function trained for. 3. The method of claim 2, further comprising smoothing of any static nonlinearities in the static representation of the node response function using a process selected from the group consisting of equation 4, an analytic smoothing function, and numerical filtering. 4. The method of claim 2, wherein the connection weights are determined using a neural compiler. 5. The method of claim 2, wherein the neural network includes an adaptive rule that optimizes initially determined weights. 6. The method of claim 1 wherein the network is trained with noise that accounts for variability introduced by neural spiking. 7. The method of claim 1, wherein the network computation includes additional nonlinearities and/or linearities. 8. A system for pattern classification, data representation, or signal processing in spiking neural networks, the system comprising: a computer processor executing computer readable instructions stored on a computer readable medium; a data structure stored on the computer readable medium, and modified based on said computer processor executing said computer readable instructions; the data structure consisting of: one or more input layers presenting a vector of 1 or greater dimensions; one or more output layers generating a vector representation of the data presented at the one or more input layers; zero or more intermediate layers coupled via weight matrices to at least one of the input layers, an intermediate, and the output layers; wherein each layer comprises a plurality of nonlinear components, wherein each nonlinear component is configured to generate a spike; where a spike is defined as temporally brief outputs at each moment in time in response to an input, and each layer is coupled to the layer's output by at least one weighted coupling; the output from each nonlinear component is weighted by the connection weights of corresponding weighted couplings and the weighted outputs are provided to the layer's output; the input to the system is either discrete or continuous in time and space; the input to the system is a scalar or a multidimensional vector. 9. The system of claim 8, wherein the connection weights are determined according to the method of claim 2. 10. The system of claim 8 including neuromorphic hardware, or software. 11. A computer system comprising computer readable instructions stored on a computer readable medium, which when executed by a computer processor: defines a node response function that exhibits spikes, where spikes are temporal nonlinearities for representing state over time; defines a static representation of said node response function; uses the static representation of the node response function to train a neural network. 12. The computer system of claim 11, wherein the computer readable instructions executed by the processor further derives neural connection weights from the using of the static representation of the node response function to train the neural network; wherein the derived connection weights are used in a dynamically implemented spiking neural network to compute the function trained for. 13. The computer system of claim 1, wherein the computer readable instructions executed by the computer processor further smooths any static nonlinearities in the static representation of the node response function using a process selected from the group consisting of equation 4, an analytic smoothing function, and numerical filtering. 14. The computer system of claim 12, wherein the connection weights are determined using a neural compiler. 15. The computer system of claim 12, wherein computer readable instructions further include an adaptive rule that optimizes initially determined weights. 16. The computer system of claim 11 wherein the computer readable instructions further cause the network to be trained with noise that accounts for variability introduced by neural spiking.