Defining dynamics of multiple neurons

1. A computer-implemented method for configuring data in a data structure for dynamically setting a neuron value for a neuron in an artificial neural network, the method comprising:

determining a number of segments to be represented in the data of the data structure comprising a set of parameters for a neuron model;

determining a number of neuron types to be represented in the set of parameters; and

determining at least one boundary in the data of the data structure to define at least one data segment in the data, the data in each segment being configured for dynamically setting the neuron value for the neuron in the artificial neural network, wherein the set of parameters are indexed according to a neuron type.

2. The method of claim 1 , further comprising determining at least one of a first state variable or a second state variable for the neuron based at least in part on the set of parameters configured in the at least one data segment, one or more of the first state variable and the second state variable being used to determine a spiking threshold for a specific neuron.

3. The method of claim 2 , in which the parameters for one or more of a membrane voltage and a recovery variable are selected based at least in part on the neuron type.

4. The method of claim 2 , in which the first state variable comprises a membrane voltage, and the second state variable comprises a recovery variable.

5. The method of claim 1 , in which the determined number of segments is variable for each neuron type.

6. The method of claim 1 , in which the at least one boundary is determined based on a function of one state variable.

7. The method of claim 1 , in which the at least one boundary is determined based on a function of at least two state variables.

8. The method of claim 1 , in which the parameters define initial neuron dynamics.

9. The method of claim 1 , in which the parameters are dynamically updated based at least in part on a firing rate, incoming synapses, synaptic weight, or a combination thereof.

10. A method for generating a neural model for an artificial neural network, the method comprising:

receiving information specifying a neuron type;

determining a set of parameter values using data of a data structure based at least in part on the neuron type, the parameter values being indexed according to the neuron type;

generating the neural model in the artificial neural network in accordance with the parameter values corresponding to the specified neuron type; and

operating the artificial neural network with the neural model.

11. An apparatus for dynamically setting a neuron value for a neuron in an artificial neural network comprising:

a memory; and

at least one processor coupled to the memory, the at least one processor being configured:

to determine a number of segments to be represented in data of a data structure comprising a set of parameters for a neuron model;

to determine a number of neuron types to be represented in the set of parameters; and

to determine at least one boundary in the data of the data structure to define at least one data segment in the data, the data in each segment being configured for dynamically setting the neuron value for the neuron in the artificial neural network, wherein the set of parameters are indexed according to a neuron type.

12. The apparatus of claim 11 , in which the at least one processor is further configured to determine at least one of a first state variable or a second state variable for the neuron based at least in part on the set of parameters configured in the at least one data segment, one or more of the first state variable and the second state variable being used to determine a spiking threshold for a specific neuron.

13. The apparatus of claim 12 , in which the parameters for one or more of a membrane voltage and a recovery variable are selected based at least in part on the neuron type.

14. The apparatus of claim 12 , in which the first state variable comprises a membrane voltage, and the second state variable comprises a recovery variable.

15. The apparatus of claim 11 , in which the determined number of segments is variable for each neuron type.

16. An apparatus for dynamically setting a neuron value for a neuron in an artificial neural network comprising:

means for determining a number of segments defined in data of a data structure comprising a set of parameters for a neuron model;

means for determining a number of neuron types defined in the set of parameters; and

means for determining at least one boundary in the data of the data structure to define at least one data segment in the data, the data in each segment being configured for dynamically setting the neuron value for the neuron in the artificial neural network, wherein the set of parameters are indexed according to a neuron type.

17. A non-transitory computer-readable medium having program code recorded thereon for dynamically setting a neuron value for a neuron in an artificial neural network, the program code being executed by a processor and comprising:

program code to determine a number of segments to be represented in data of a data structure comprising a set of parameters for a neuron model in the set of parameters;

program code to determine a number of neuron types to be represented in the set of parameters; and

program code to determine at least one boundary in the data of the data structure to define at least one data segment in the data, the data in each segment being configured for dynamically setting the neuron value for the neuron in the artificial neural network, wherein the set of parameters are indexed according to a neuron type.

18. An apparatus for generating a neural model for an artificial neural network comprising:

a memory; and

at least one processor coupled to the memory, the at least one processor being configured:

to receive information specifying a neuron type;

to determine a set of parameter values using data of a data structure based at least in part on the neuron type, the parameter values being indexed according to the neuron type;

to generate the neural model in the artificial neural network in accordance with the parameter values corresponding to the specified neuron type; and

operating the artificial neural network with the neural model.

19. An apparatus for generating a neural model for an artificial neural network comprising:

means for receiving information specifying a neuron type;

means for determining a set of parameter values using data of a data structure based at least in part on the neuron type, the parameter values being indexed according to the neuron type;

means for generating the neural model in the artificial neural network in accordance with the parameter values corresponding to the specified neuron type; and

means for operating the artificial neural network with the neural model.

20. A non-transitory computer-readable medium having program code recorded thereon for generating a neural model for an artificial neural network, the program code being executed by a processor and comprising:

program code to receive information specifying a neuron type;

program code to determine a set of parameter values using data of a data structure based at least in part on the neuron type, the parameter values being indexed according to the neuron type;

program code to generate the neural model in the artificial neural network in accordance with the parameter values corresponding to the specified neuron type; and

operating the artificial neural network with the neural model.