A neural system comprises multiple neurons interconnected via synapse devices. Each neuron integrates input signals arriving on its dendrite, generates a spike in response to the integrated input signals exceeding a threshold, and sends the spike to the interconnected neurons via its axon. The system further includes multiple noruens, each noruen is interconnected via the interconnect network with those neurons that the noruen's corresponding neuron sends its axon to. Each noruen integrates input spikes from connected spiking neurons and generates a spike in response to the integrated input spikes exceeding a threshold. There can be one noruen for every corresponding neuron. For a first neuron connected via its axon via a synapse to dendrite of a second neuron, a noruen corresponding to the second neuron is connected via its axon through the same synapse to dendrite of the noruen corresponding to the first neuron.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for reinforcement learning, the method comprising: receiving at least one reinforcement signal from at least one spiking neuron of a neural network comprising multiple neurons interconnected by multiple synapse devices, wherein each reinforcement signal corresponds to a past event involving an action, and the reinforcement signal represents a reward indicating success of the action; determining at least one synapse device of the multiple synapse devices contributing to spiking of the at least one spiking neuron; and selectively updating synaptic weights of the multiple synapse devices to strengthen the at least one synapse device determined, wherein the updated synaptic weights allows selection of at least one action that increases rewards over time. 2. The method of claim 1, further comprising: selectively updating the synaptic weights of the multiple synapse devices to strengthen the at least one synapse device determined based on a first learning rule. 3. The method of claim 2, wherein the first learning rule implements spike-timing dependent plasticity (STDP). 4. The method of claim 3, further comprising: receiving a spike signal from a spiking neuron of the neural network, wherein the spike signal represents occurrence of a desirable event; determining a synapse device of the multiple synapse devices contributing to spiking of the spiking neuron; and strengthening a synaptic weight of the synapse device determined. 5. The method of claim 3, further comprising: receiving a spike signal from a spiking neuron of the neural network, wherein the spike signal represents occurrence of an undesirable event; determining a synapse device of the multiple synapse devices contributing to spiking of the spiking neuron; and weakening a synaptic weight of the synapse device determined. 6. The method of claim 5, further comprising: weakening the synaptic weight of the synapse device determined based on a second learning rule. 7. The method of claim 6, wherein the second learning rule implements anti-STDP. 8. A system comprising a computer processor, a computer-readable hardware storage device, and program code embodied with the computer-readable hardware storage device for execution by the computer processor to implement a method for reinforcement learning, the method comprising: receiving at least one reinforcement signal from at least one spiking neuron of a neural network comprising multiple neurons interconnected by multiple synapse devices, wherein each reinforcement signal corresponds to a past event involving an action, and the reinforcement signal represents a reward indicating success of the action; determining at least one synapse device of the multiple synapse devices contributing to spiking of the at least one spiking neuron; and selectively updating synaptic weights of the multiple synapse devices to strengthen the at least one synapse device determined, wherein the updated synaptic weights allows selection of at least one action that increases rewards over time. 9. The system of claim 8, the method further comprising: selectively updating the synaptic weights of the multiple synapse devices to strengthen the at least one synapse device determined based on a first learning rule. 10. The system of claim 9, wherein the first learning rule implements spike-timing dependent plasticity (STDP). 11. The system of claim 10, the method further comprising: receiving a spike signal from a spiking neuron of the neural network, wherein the spike signal represents occurrence of a desirable event; determining a synapse device of the multiple synapse devices contributing to spiking of the spiking neuron; and strengthening a synaptic weight of the synapse device determined. 12. The system of claim 10, the method further comprising: receiving a spike signal from a spiking neuron of the neural network, wherein the spike signal represents occurrence of an undesirable event; determining a synapse device of the multiple synapse devices contributing to spiking of the spiking neuron; and weakening a synaptic weight of the synapse device determined. 13. The system of claim 12, the method further comprising: weakening the synaptic weight of the synapse device determined based on a second learning rule. 14. The system of claim 13, wherein the second learning rule implements anti-STDP. 15. A computer program product comprising a computer-readable hardware storage device having program code embodied therewith, the program code being executable by a computer to implement a method for reinforcement learning, the method comprising: receiving at least one reinforcement signal from at least one spiking neuron of a neural network comprising multiple neurons interconnected by multiple synapse devices, wherein each reinforcement signal corresponds to a past event involving an action, and the reinforcement signal represents a reward indicating success of the action; determining at least one synapse device of the multiple synapse devices contributing to spiking of the at least one spiking neuron; and selectively updating synaptic weights of the multiple synapse devices to strengthen the at least one synapse device determined, wherein the updated synaptic weights allows selection of at least one action that increases rewards over time. 16. The computer program product of claim 15, the method further comprising: selectively updating the synaptic weights of the multiple synapse devices to strengthen the at least one synapse device determined based on a first learning rule. 17. The computer program product of claim 16, wherein the first learning rule implements spike-timing dependent plasticity (STDP). 18. The computer program product of claim 17, the method further comprising: receiving a spike signal from a spiking neuron of the neural network, wherein the spike signal represents occurrence of a desirable event; determining a synapse device of the multiple synapse devices contributing to spiking of the spiking neuron; and strengthening a synaptic weight of the synapse device determined. 19. The computer program product of claim 17, the method further comprising: receiving a spike signal from a spiking neuron of the neural network, wherein the spike signal represents occurrence of an undesirable event; determining a synapse device of the multiple synapse devices contributing to spiking of the spiking neuron; and weakening a synaptic weight of the synapse device determined. 20. The computer program product of claim 19, the method further comprising: weakening the synaptic weight of the synapse device determined based on a second learning rule, wherein the second learning rule implements anti-STDP.