Embodiments of the invention relate to a neural network system for simulating neurons of a neural model. One embodiment comprises a memory device that maintains neuronal states for multiple neurons, a lookup table that maintains state transition information for multiple neuronal states, and a controller unit that manages the memory device. The controller unit updates a neuronal state for each neuron based on incoming spike events targeting said neuron and state transition information corresponding to said neuronal state.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising a computer processor, a computer-readable hardware storage device, and program code embodied with the computer-readable hardware storage device for execution by the computer processor to implement a method for simulating slowest and fastest neural dynamics of a neural model, the method comprising: maintaining neuronal states for multiple neurons in a memory device, wherein each neuron represents an instance of said neural model, and each neuronal state is expressed by one or more variables of said neural model; maintaining neuronal state transition information representing said slowest and fastest neural dynamics of said neural model in a delta-based lookup table, wherein said neuronal state transition information comprises delta values for at least one variable of said neural model; and updating a neuronal state for a neuron based on incoming spike events targeting said neuron and said neuronal state transition information. 2. The system of claim 1, wherein: said lookup table represents a sequence of neuronal states; and for each neuronal state, said neuronal state transition information includes at least one delta value for at least one variable of said neural model, wherein said at least one delta value is added to said neuronal state to obtain a subsequent neuronal state that said neuronal state transitions to. 3. The system of claim 2, wherein updating a neuronal state for a neuron based on incoming spike events targeting said neuron and said neuronal state transition information comprises updating said neuronal state to a subsequent neuronal state that said neuronal state transitions to. 4. The system of claim 2, the method further comprising: addressing said lookup table by a neuronal state. 5. The system of claim 4, wherein addressing said lookup table by a neuronal state comprises addressing said lookup table using one or more variables of said neural model that expresses said neuronal state. 6. The system of claim 5, wherein addressing said lookup table using one or more variables of said neural model comprises combining most significant bits of said one or more variables. 7. The system of claim 2, wherein said at least one variable is a membrane potential variable. 8. A method for simulating slowest and fastest neural dynamics of a neural model, comprising: maintaining neuronal states for multiple neurons in a memory device, wherein each neuron represents an instance of said neural model, and each neuronal state is expressed by one or more variables of said neural model; maintaining neuronal state transition information representing said slowest and fastest neural dynamics of said neural model in a delta-based lookup table, wherein said neuronal state transition information comprises delta values for at least one variable of said neural model; and updating a neuronal state for a neuron based on incoming spike events targeting said neuron and said neuronal state transition information. 9. The method of claim 8, wherein: said lookup table represents a sequence of neuronal states; and for each neuronal state, said neuronal state transition information includes at least one delta value for at least one variable of said neural model, wherein said at least one delta value is added to said neuronal state to obtain a subsequent neuronal state that said neuronal state transitions to. 10. The method of claim 9, wherein updating a neuronal state for a neuron based on incoming spike events targeting said neuron and said neuronal state transition information comprises updating said neuronal state to a subsequent neuronal state that said neuronal state transitions to. 11. The method of claim 9, further comprising: addressing said lookup table by a neuronal state. 12. The method of claim 11, wherein addressing said lookup table by a neuronal state comprises addressing said lookup table using one or more variables of said neural model that expresses said neuronal state. 13. The method of claim 12, wherein addressing said lookup table using one or more variables of said neural model comprises combining most significant bits of said one or more variables. 14. The method of claim 9, wherein said at least one variable is a membrane potential variable. 15. A computer program product comprising a computer-readable hardware storage device having program code embodied therewith, the program code being executable by a computer to implement a method for simulating slowest and fastest neural dynamics of a neural model, the method comprising: maintaining neuronal states for multiple neurons in a memory device, wherein each neuron represents an instance of said neural model, and each neuronal state is expressed by one or more variables of said neural model; maintaining neuronal state transition information representing said slowest and fastest neural dynamics of said neural model in a delta-based lookup table, wherein said neuronal state transition information comprises delta values for at least one variable of said neural model; and updating a neuronal state for a neuron based on incoming spike events targeting said neuron and said neuronal state transition information. 16. The program product of claim 15, wherein: said lookup table represents a sequence of neuronal states; and for each neuronal state, said neuronal state transition information includes at least one delta value for at least one variable of said neural model, wherein said at least one delta value is added to said neuronal state to obtain a subsequent neuronal state that said neuronal state transitions to. 17. The program product of claim 16, wherein updating a neuronal state for a neuron based on incoming spike events targeting said neuron and said neuronal state transition information comprises updating said neuronal state to a subsequent neuronal state that said neuronal state transitions to. 18. The program product of claim 16, the method further comprising: addressing said lookup table by a neuronal state. 19. The program product of claim 18, wherein addressing said lookup table by a neuronal state comprises addressing said lookup table using one or more variables of said neural model that expresses said neuronal state. 20. The program product of claim 19, wherein addressing said lookup table using one or more variables of said neural model comprises combining most significant bits of said one or more variables.