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: at least one processor; and a non-transitory processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations including: maintaining different look-up tables comprising information on changes in neuronal states for different neural models; and multiplexing the different look-up tables to simulate neurons exhibiting different neural dynamics of the different neural models. 2. The system of claim 1, wherein the operations further comprise: maintaining neuronal states for the neurons, wherein each neuron represents an instance of a neural model of the different neural models, and each neuronal state is expressed by one or more variables of the neural model. 3. The system of claim 2, wherein each look-up table corresponds to a neural model of the different neural models, and the look-up table includes neuronal state transition information representing slowest and fastest neural dynamics of the neural model. 4. The system of claim 3, wherein each look-up table comprises a delta-based lookup table including delta values for at least one variable of a corresponding neural model. 5. The system of claim 3, wherein the operations further comprise: updating a neuronal state of a neuron of the neurons based on incoming spike events targeting the neuron and a look-up table corresponding to a neural model that the neuron represent an instance of. 6. A method comprising: maintaining different look-up tables comprising information on changes in neuronal states for different neural models; and multiplexing the different look-up tables to simulate neurons exhibiting different neural dynamics of the different neural models. 7. The method of claim 6, further comprising: maintaining neuronal states for the neurons, wherein each neuron represents an instance of a neural model of the different neural models, and each neuronal state is expressed by one or more variables of the neural model. 8. The method of claim 7, wherein each look-up table corresponds to a neural model of the different neural models, and the look-up table includes neuronal state transition information representing slowest and fastest neural dynamics of the neural model. 9. The method of claim 8, wherein each look-up table comprises a delta-based lookup table including delta values for at least one variable of a corresponding neural model. 10. The method of claim 8, further comprising: updating a neuronal state of a neuron of the neurons based on incoming spike events targeting the neuron and a look-up table corresponding to a neural model that the neuron represent an instance of. 11. A non-transitory computer readable storage medium including instructions to perform a method comprising: maintaining different look-up tables comprising information on changes in neuronal states for different neural models; and multiplexing the different look-up tables to simulate neurons exhibiting different neural dynamics of the different neural models. 12. The computer readable storage medium of claim 11, the method further comprising: maintaining neuronal states for the neurons, wherein each neuron represents an instance of a neural model of the different neural models, and each neuronal state is expressed by one or more variables of the neural model. 13. The computer readable storage medium of claim 12, wherein each look-up table corresponds to a neural model of the different neural models, and the look-up table includes neuronal state transition information representing slowest and fastest neural dynamics of the neural model. 14. The computer readable storage medium of claim 13, wherein each look-up table comprises a delta-based lookup table including delta values for at least one variable of a corresponding neural model. 15. The computer readable storage medium of claim 13, the method further comprising: updating a neuronal state of a neuron of the neurons based on incoming spike events targeting the neuron and a look-up table corresponding to a neural model that the neuron represent an instance of.