A method for managing synapse plasticity in a neural network includes converting a first set of synapses from a plastic synapse type to a fixed synapse type. The method may also include converting a second set of synapses from the fixed synapse type to the plastic synapse type.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for managing synapse plasticity in a neural network, comprising converting one or more of, a first set of synapses from a plastic synapse type to a fixed synapse type, a second set of synapses from the fixed synapse type to the plastic synapse type, or a combination thereof. 2. The method of claim 1, in which the converting is based at least in part on one or more of a user input, power management, computation time management, a minimum number of synapse instances, a synapse training interval, a synapse type, an overall training need of the neural network, or a combination thereof. 3. The method of claim 1, in which the second set of synapses is designated to be trained in a neural model. 4. The method of claim 1, in which a number of plastic synapse types is pre-defined. 5. The method of claim 1, in which the converting is controlled by a resource manager. 6. The method of claim 1, further comprising changing properties of the first set of synapses and the second set of synapses based on outputs of a neural network and/or behavior of a neural network. 7. The method of claim 1, further comprising turning on or off plasticity related computation in a neuron when all outgoing and/or incoming synapses of the neuron are non-plastic synapses. 8. An apparatus for managing synapse plasticity in a neural network, comprising: a memory unit; and at least one processor coupled to the memory unit, the at least one processor being configured: to convert one or more of, a first set of synapses from a plastic synapse type to a fixed synapse type, a second set of synapses from the fixed synapse type to the plastic synapse type, or a combination thereof. 9. The apparatus of claim 8, in which the at least one processor is further configured to convert based at least in part on one or more of a user input, power management, computation time management, a minimum number of synapse instances, a synapse training interval, a synapse type, an overall training need of the neural network, or a combination thereof. 10. The apparatus of claim 8, in which the second set of synapses is designated to be trained in a neural model. 11. The apparatus of claim 8, in which a number of plastic synapse types is pre-defined. 12. The apparatus of claim 8, in which the converting is controlled by a resource manager. 13. The apparatus of claim 8, in which the at least one processor is further configured to change properties of the first set of synapses and the second set of synapses based on outputs of a neural network and/or behavior of a neural network. 14. The apparatus of claim 8, in which the at least one processor is further configured to turn on or off plasticity related computation in a neuron when all outgoing and/or incoming synapses of the neuron are non-plastic synapses. 15. An apparatus for managing synapse plasticity in a neural network, comprising means for determining a number of plastic synapse types defined for a system; and means for converting one or more of, a first set of synapses from a plastic synapse type to a fixed synapse type, a second set of synapses from the fixed synapse type to the plastic synapse type, or a combination thereof. 16. The apparatus of claim 15, in which the means for converting is based at least in part on one or more of a user input, power management, computation time management, a minimum number of synapse instances, a synapse training interval, a synapse type, an overall training need of the neural network, or a combination thereof. 17. The apparatus of claim 16, in which the second set of synapses is designated to be trained in a neural model. 18. A computer program product for wireless communications, the computer program product comprising: a non-transitory computer-readable medium having program code recorded thereon, the program code comprising: program code to convert one or more of, a first set of synapses from a plastic synapse type to a fixed synapse type, a second set of synapses from the fixed synapse type to the plastic synapse type, or a combination thereof. 19. The computer program product of claim 18, in which the program code further comprises code to convert based at least in part on one or more of a user input, power management, computation time management, a minimum number of synapse instances, a synapse training interval, a synapse type, an overall training need of the neural network, or a combination thereof. 20. The computer program product of claim 18, in which the second set of synapses is designated to be trained in a neural model.