A method of dynamically modifying target selection with a neural network includes dynamically modifying a selection function by controlling an amount of imbalance of connections in the neural network. A selected neuron represents one of multiple candidate targets.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of dynamically modifying target selection with a neural network, comprising: dynamically modifying a selection function by controlling an amount of imbalance of connections in the neural network, a selected neuron representing one of a plurality of candidate targets. 2. The method of claim 1, in which the connections comprise inter-neurons coupled between each pair of input and output neurons, and the dynamically modifying comprises at least dynamically modifying excitability of the inter-neurons, dynamically modifying efficacy of synaptic weights between the inter-neurons and the input and output neurons, or a combination thereof. 3. The method claim 2, in which the dynamically modifying comprises dynamically modifying with an external input. 4. The method of claim 1, in which the dynamically modifying comprises at least dynamically modifying excitability of a neuron, dynamically modifying efficacy of synaptic weights, or a combination thereof. 5. The method claim 4, in which the synaptic weights are for at least input synapses, connections between neurons, or a combination thereof. 6. The method of claim 1, in which the selection function is based at least in part on an inhibition on return, a behavioral state of an object that selects the target, a period of time, an assigned task, a time multiplexing function, an event that selects a task from a plurality of tasks corresponding to a plurality of neural networks, or a combination thereof. 7. The method of claim 6, in which each task corresponds to one of the plurality of neural networks, and each neural network is a static network or a dynamic network. 8. The method of claim 1, further comprising controlling the amount of imbalance of connections in the neural network based at least on inputs to the neural network, outputs from the neural network, or a combination thereof. 9. An apparatus for dynamically modifying target selection with a neural network, the apparatus comprising: a memory unit; and at least one processor coupled to the memory unit, the at least one processor being configured to dynamically modify a selection function by controlling an amount of imbalance of connections in the neural network, a selected neuron representing one of a plurality of candidate targets. 10. The apparatus of claim 9, in which the connections comprise inter-neurons coupled between each pair of input and output neurons, and the at least one processor being further configured to dynamically modify at least excitability of the inter-neurons, efficacy of synaptic weights between the inter-neurons and the input and output neurons, or a combination thereof. 11. The apparatus claim 10, in which the at least one processor is further configured to dynamically modify with an external input. 12. The apparatus of claim 9, in which the at least one processor is further configured to dynamically modify at least excitability of a neuron, efficacy of synaptic weights, or a combination thereof. 13. The apparatus claim 12, in which the synaptic weights are for at least input synapses, connections between neurons, or a combination thereof. 14. The apparatus of claim 9, in which the selection function is based at least in part on an inhibition on return, a behavioral state of an object that selects the target, a period of time, an assigned task, a time multiplexing function, an event that selects a task from a plurality of tasks corresponding to a plurality of neural networks, or a combination thereof. 15. The apparatus of claim 14, in which each task corresponds to one of the plurality of neural networks, and each neural network is a static network or a dynamic network. 16. The apparatus of claim 9, in which the at least one processor is further configured to control the amount of imbalance of connections in the neural network based at least on inputs to the neural network, outputs from the neural network, or a combination thereof. 17. An apparatus, the apparatus comprising: means for determining an amount of imbalance of connections in a neural network; and means for dynamically modify a selection function by controlling the amount of imbalance of connections in the neural network, a selected neuron representing one of a plurality of candidate targets. 18. The apparatus of claim 17, in which the connections comprise inter-neurons coupled between each pair of input and output neurons, and the means for dynamically modifying comprises means for dynamically modifying at least excitability of the inter-neurons, efficacy of synaptic weights between the inter-neurons and the input and output neurons, or a combination thereof. 19. The apparatus claim 18, in which the means for dynamically modifying comprises means for dynamically modifying with an external input. 20. The apparatus of claim 17, in which the means for dynamically modifying comprises means for dynamically modifying at least excitability of a neuron, modifying efficacy of synaptic weights, or a combination thereof. 21. The apparatus claim 20, in which the synaptic weights are for at least input synapses, connections between neurons, or a combination thereof. 22. The apparatus of claim 17, in which the selection function is based at least in part on an inhibition on return, a behavioral state of an object that selects the target, a period of time, an assigned task, a time multiplexing function, an event that selects a task from a plurality of tasks corresponding to a plurality of neural networks, or a combination thereof. 23. The apparatus of claim 22, in which each task corresponds to one of the plurality of neural networks, and each neural network is a static network or a dynamic network. 24. The apparatus of claim 17, further comprising means for controlling the amount of imbalance of connections in the neural network based at least on inputs to the neural network, outputs from the neural network, or a combination thereof. 25. A computer program product for dynamically modifying target selection with a neural network, comprising: a non-transitory computer-readable medium having non-transitory program code recorded thereon, the program code comprising program code to dynamically modify a selection function by controlling an amount of imbalance of connections in the neural network, a selected neuron representing one of a plurality of candidate targets. 26. The computer program product of claim 25, in which the connections comprise inter-neurons coupled between each pair of input and output neurons, and the program code further comprises program code to dynamically modify at least excitability of the inter-neurons, efficacy of synaptic weights between the inter-neurons and the input and output neurons, or a combination thereof. 27. The computer program product claim 26, in which the program code further comprises program code to dynamically modify with an external input. 28. The computer program product of claim 25, in which program code further comprises program code to dynamically modify at least excitability of a neuron, efficacy of synaptic weights, or a combination thereof. 29. The computer program product claim 28, in which the synaptic weights are for at least input synapses, connections between neurons, or a combination thereof. 30. The computer program product of claim 25, in which the selection function is based at least in part on an inhibition on return, a behavioral state of an object that selects the target, a period of time, an assigned task, a time multiplexing function, an event that selects a task from a plurality of tasks corresponding to a plurality of neural networks, or a combination thereof.