A method for selecting a neuron model with a user defined firing rate for operating in a neural network includes selecting the neuron model based on a selected firing rate bandwidth.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for selecting a neuron model with a user defined firing rate for operating in a neural network, comprising: selecting the neuron model based at least in part on a selected firing rate bandwidth. 2. The method of claim 1, in which the firing rate bandwidth is selected based at least in part on a difference between a selected minimum firing rate at which the neural network will operate and a selected maximum firing rate at which the neural network will operate. 3. The method of claim 2, in which the minimum firing rate is based at least in part on a rate of change of a model generated neural response function with respect to an input current. 4. The method of claim 2 in which the selecting is based at least in part on satisfying a condition that a generated neural response function for the selected neuron model is less than or equal to the selected minimum firing rate. 5. The method of claim 1, in which the neuron model is selected based at least in part on an input current and a spiking threshold. 6. The method of claim 1, in which the neural network is based at least in part on a neural engineering framework. 7. The method of claim 1, in which the neuron model is selected from a group comprising a Leaky Integrate and Fire (LIF) neuron model, a Quadratic Integrate Fire (QIF) neuron model, a map based neuron model, a Linear Threshold (LT) neuron model, a differential equation based neuron model and a Cold neuron Model. 8. An apparatus for selecting a neuron model with a user defined firing rate for operating in a neural network, comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured: to select the neuron model based at least in part on a selected firing rate bandwidth. 9. The apparatus of claim 8, in which the at least one processor is further configured to select the firing rate bandwidth based at least in part on a difference between a selected minimum firing rate at which the neural network will operate and a selected maximum firing rate at which the neural network will operate. 10. The apparatus of claim 9, in which the at least one processor is further configured to select the minimum firing rate based at least in part on a rate of change of a model generated neural response function with respect to an input current. 11. The apparatus of claim 9, in which the at least one processor is further configured to select the neuron model based at least in part on satisfying a condition that a generated neural response function for the selected neuron model is less than or equal to the selected minimum firing rate. 12. The apparatus of claim 8, in which the at least one processor is further configured to select the neuron model based at least in part on an input current and a spiking threshold. 13. The apparatus of claim 8, in which the neural network is based at least in part on a neural engineering framework. 14. The apparatus of claim 8, in which the at least one processor is further configured to select the neuron model from a group comprising a Leaky Integrate and Fire (LIF) neuron model, a Quadratic Integrate Fire (QIF) neuron model, a map based neuron model, a Linear Threshold (LT) neuron model, a differential equation based neuron model and a Cold neuron Model. 15. An apparatus for selecting a neuron model with a user defined firing rate for operating in a neural network, comprising: means for selecting a firing rate bandwidth based at least in part on a difference between a selected minimum firing rate at which the neural network will operate and a selected maximum firing rate at which the neural network will operate; and means for selecting the neuron model based at least in part on the selected firing rate bandwidth. 16. The apparatus of claim 15, in which the minimum firing rate is based at least in part on a rate of change of a model generated neural response function with respect to an input current. 17. The apparatus of claim 15, in which the means for selecting the neuron model selects the neuron model based at least in part on satisfying a condition that a generated neural response function for the selected neuron model is less than or equal to the selected minimum firing rate. 18. The apparatus of claim 15, in which the means for selecting the neuron model selects the neuron model based at least in part on an input current and a spiking threshold. 19. The apparatus of claim 15, in which the neural network is based at least in part on a neural engineering framework. 20. The apparatus of claim 15, in which the means for selecting the neuron model selects the neuron model from a group comprising a Leaky Integrate and Fire (LIF) neuron model, a Quadratic Integrate Fire (QIF) neuron model, a map based neuron model, a Linear Threshold (LT) neuron model, a differential equation based neuron model and a Cold neuron Model. 21. A computer program product for selecting a neuron model with a user defined firing rate for operating in a neural network, comprising: a non-transitory computer readable medium having encoded thereon program code, the program code comprising: program code to select the neuron model based at least in part on a selected firing rate bandwidth. 22. The computer program product of claim 21, further comprising program code to select the firing rate bandwidth based at least in part on a difference between a selected minimum firing rate at which the neural network will operate and a selected maximum firing rate at which the neural network will operate. 23. The computer program product of claim 22, further comprising program code to select the minimum firing rate based at least in part on a rate of change of a model generated neural response function with respect to an input current. 24. The computer program product of claim 22, further comprising program code to select the neuron model based at least in part on satisfying a condition that a generated neural response function for the selected neuron model is less than or equal to the selected minimum firing rate. 25. The computer program product of claim 21, further comprising program code to select the neuron model based at least in part on an input current and a spiking threshold. 26. The computer program product of claim 21, in which the neural network is based at least in part on a neural engineering framework. 27. The computer program product of claim 21, further comprising program code to select the neuron model from a group comprising a Leaky Integrate and Fire (LIF) neuron model, a Quadratic Integrate Fire (QIF) neuron model, a map based neuron model, a Linear Threshold (LT) neuron model, a differential equation based neuron model and a Cold neuron Model.