A method is described for designing systems that provide efficient implementations of feed-forward, recurrent, and deep networks that process dynamic signals using temporal filters and static or time-varying nonlinearities. A system design methodology is described that provides an engineered architecture. This architecture defines a core set of network components and operations for efficient computation of dynamic signals using temporal filters and static or time-varying nonlinearities. These methods apply to a wide variety of connected nonlinearities that include temporal filters in the connections. Here we apply the methods to synaptic models coupled with spiking and/or non-spiking neurons whose connection parameters are determined using a variety of methods of optimization.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for implementing an artificial neural network for dynamic computing applications, the artificial neural network comprising a plurality of nodes, each node having an input and an output; the method comprising: defining a node response function which receives varying inputs over time at each node input and produces varying outputs over time at each node output; defining at least one temporal filter associated with each node input and/or output; using the node response function and each said temporal filter to determine connection weights between layers of nodes in the artificial neural network; computing a dynamic function based on the determined connection weights between layers of nodes in the artificial neural network. 2. The method of claim 1, wherein said node response function exhibits either spikes in a period of time or a firing rate over a period of time. 3. The method of claim 1, wherein the neural network is one of a single or multi-layer network and one of a feed-forward or recurrent network. 4. The method of claim 1, wherein the connection weights are determined using either an online or offline optimization method. 5. The method of claim 1, wherein the connection weights are factorized. 6. The method of claim 1, wherein the at least one filter is an arbitrary synaptic filter. 7. The method of claim 1, wherein the dynamic function being computed optionally includes a delay. 8. The method of claim 7, wherein the delay is a controllable delay. 9. The method of claim 7, further comprising a bank of controlled or non-controlled delays. 10. The method of claim 1, wherein the dynamic function is approximated using Padé approximants. 11. A system for pattern classification, data representation or signal processing in artificial neural networks 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: at least one input layer defining a vector; at least one output layer generating a vector representation of data presented at the at least one input layer or computing a function of the data presented at the at least one input layer; wherein each of the input and output layers comprises a plurality of nodes; each node having at least one temporal filter on its input and/or output; each node coupled to the layer's input and output by at least one weighted coupling; the output from each node's temporal filter is weighted by connection weights of the corresponding weighted couplings; and, the weighted and filtered inputs are communicated back to each node. 12. The system of claim 11, further comprising at least one intermediate layer coupled via weight matrices to the at least one input layer and to the at least one output layer. 13. The system of claim 12, wherein the at least one intermediate layer comprises a first intermediate layer is coupled via weight matrices to the at least one input layer, at least one second intermediate layer coupled via weight matrices to the first intermediate layer, and at least one third intermediate layer coupled via weight matrices to the at least one output layer. 14. The system of claim 11 wherein the connection weights are determine using either an online or offline optimization method. 15. The system of claim 11, wherein the connection weights are factorized. 16. The system of claim 11, wherein the at least one filter is an arbitrary synaptic filter. 17. The system of claim 11, wherein the dynamic function being computed includes a delay. 18. The system of claim 17, wherein the delay is a controllable delay. 19. The system of claim 17, further comprising a bank of controlled or non-controlled delays. 20. The system of claim 11, wherein the dynamic function is approximated using Padé approximants. 21. The system of claim 11, wherein the input is either discrete or continuous in time and space. 22. The system of claim 11, wherein the input is a scalar or a multi-dimensional vector. 23. The system of claim 11, wherein the computer readable instructions are implemented in software or in hardware. 24. The system of claim 23, wherein hardware is selected from the group consisting of neuromorphic hardware, digital hardware, analog hardware and combinations thereof.