A spiking neural network (SNN) includes artificial neurons interconnected by artificial synapses to model a particular network. A first neuron emits spikes to neighboring neurons to cause a wave of spikes to propagate through the SNN. Weights of a portion of the synapses are increased responsive to the wave of spikes based on a spike timing dependent plasticity (STDP) rule. A second neuron emits spike to cause a chain of spikes to propagate to the first neuron on a path based on the increase in the synaptic weights. The path is determined to represent a shortest path in the particular network from a first network node represented by the second neuron to a second network node represented by the first neuron.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. At least one machine accessible storage medium having instructions stored thereon, wherein the instructions when executed on a machine, cause the machine to: define a spiking neural network (SNN) to be implemented on a neuromorphic computing device, wherein the SNN comprises a plurality of artificial neurons and a plurality of artificial synapses to bi-directionally connect the plurality of neurons to model a particular network; send a first signal to a first one of the plurality of neurons, wherein the first signal is to cause the first neuron to emit one or more first spikes to neighboring neurons in the SNN and cause a wave of spikes to propagate through the SNN, and weights of a portion of the plurality of synapses are to increase responsive to the wave of spikes based on a spike timing dependent plasticity (STDP) rule defined for the SNN; send a second signal to a second one of the plurality of neurons, wherein the second signal is to cause the second neuron to emit one or more second spikes to neighboring neurons in the SNN, the one or more second spikes are to cause a chain of spikes to propagate to the first neuron on a path comprising a subset of the portion of synapses based on the increase in the weights of the portion of synapses; and determine that the path comprises a shortest path in the particular network from a first network node represented by the second neuron to a second network node represented by the first neuron. 2. The storage medium of claim 1, wherein the instructions, when executed, further cause the machine to: send a neuromodulatory signal to the plurality of neurons to increase a spike potential threshold of each of the plurality of neurons, wherein the neuromodulatory signal is to be sent after sending of the first signal, and propagation of the chain of spikes on the subset of synapses is further based on the increase in the spike potential threshold. 3. The storage medium of claim 1, wherein the subset of synapses are to transmit spikes in a direction opposite the propagation of the spike wave. 4. The storage medium of claim 1, wherein each bidirectional connection between two neighboring neurons in the plurality of neurons comprises a respective synapse pair, wherein each synapse pair connecting two neighboring neurons comprises a respective first synapse from a first one of the neighboring neurons to a second one of the neighboring neurons and a respective second synapse from the second neighboring neuron to the first neighboring neuron. 5. The storage medium of claim 4, wherein spikes in the spike wave are transmitted on at least one of the synapses in each synapse pair in the SNN, and the weight is increased according to the STDP rule only for the synapse in a synapse pair other than the synapse in the synapse pair that first transmitted one of the spikes in the spike wave. 6. The storage medium of claim 5, wherein STDP rule specifies that the weight of a presynaptic synapse of a given neuron is to increase if a spike arrives on the presynaptic synapse within a time duration following a spike being sent on a postsynaptic synapse of the given neuron. 7. The storage medium of claim 1, wherein weights of the plurality of synapses are set to a common value prior to initiation of the spike wave. 8. The storage medium of claim 7, wherein receiving a spike in the spike wave is to cause any one of the plurality of neurons to be activated and send an outgoing spike to one or more respective neighboring neurons in the SNN unless the neuron is in a refractory period, and the refractory period is implemented for each of the plurality of neurons. 9. The storage medium of claim 7, wherein the instructions, when executed, further cause the machine to send a neuromodulatory signal after propagation of the spike chain to reset weights of the plurality of synapses. 10. The storage medium of claim 9, wherein the instructions, when executed, further cause the machine to send another signal to activate third one of the plurality of neurons, wherein activation of the third neuron causes propagation of another spike wave in the SNN outward from the third neuron, and weights of a different subset of the plurality of synapses are to be increased based on the propagation of the other spike wave. 11. A method comprising: defining a spiking neural network (SNN) to be implemented on a neuromorphic computing device, wherein the SNN comprises a plurality of artificial neurons and a plurality of artificial synapses to bi-directionally connect the plurality of neurons to model a particular network; sending a first signal to a first one of the plurality of neurons, wherein the first signal is to cause the first neuron to emit one or more first spikes to neighboring neurons in the SNN and cause a wave of spikes to propagate through the SNN, and weights of a portion of the plurality of synapses are to increase responsive to the wave of spikes based on a spike timing dependent plasticity (STDP) rule defined for the SNN; sending a second signal to a second one of the plurality of neurons, wherein the second signal is to cause the second neuron to emit one or more second spikes to neighboring neurons in the SNN, the one or more second spikes are to cause a chain of spikes to propagate to the first neuron on a path comprising a subset of the portion of synapses based on the increase in the weights of the portion of synapses; and determining that the path comprises a shortest path in the particular network from a first network node represented by the second neuron to a second network node represented by the first neuron. 12. An apparatus comprising: a neuromorphic computing device comprising: a routing fabric; a plurality of neuromorphic cores, wherein at least a subset of the plurality of neuromorphic cores are to respectively implement one or more of a plurality of artificial neurons in a spiking neural network (SNN) to model a particular network topology, and each neuron in the SNN models a respective one of a plurality of nodes within the particular network topology; wherein the neuromorphic computing device is to: route a first signal to activate a first one of a plurality of neurons, wherein the first neuron corresponds to a first one of the plurality of nodes; generate a spike wave based on activation of the first neuron, wherein the spike wave is to propagate from the first neuron to propagate to other neurons in the plurality of neurons on a first subset of artificial synapses in the SNN, wherein the artificial synapses are implemented using the routing fabric; adjust synaptic weights of a second subset of the synapses based on propagation of the spike wave and a spike timing dependent plasticity (STDP) rule, and the second subset of synapses comprise synapses in the SNN other than the first subset of synapses; route a second signal to activate a second one of the plurality of artificial neurons, wherein the second neuron corresponds to a second one of the plurality of nodes; generate a spike chain originating from the second neuron based on the second signal, wherein the spike chain is to propagate along a path from the second neuron to the first neuron, and the path comprises a portion of the second subset of synapses. 13. The apparatus of claim 12, wherein the particular network topology is one of a random network topology, a small world network topology, a scale free network topology, a hierarchical network topology, and a spatially embedded network topology. 14. The apparatus of claim 12, wherein the particular network topology models a physical environment. 15. The apparatus of claim 12, wherein the neuromorphic computing device further comprises logic to generate path data to describe the path. 16. The apparatus of claim 15, wherein the neuromorphic computing device further comprises an interface to output the path data to another system. 17. The apparatus of claim 12, wherein the plurality of neuromorphic cores are interconnected by the routing fabric as a mesh of neuromorphic cores. 18. The apparatus of claim 12, wherein the neuromorphic computing device comprises one or more memory elements to store one or more routing tables and at least a portion of the synapses of the SNN are defined using the one or more routing tables. 19. The apparatus of claim 12, wherein each neuromorphic core in the plurality of neuromorphic cores comprises a respective processing resource and logic to implement one or more artificial neurons. 20. The apparatus of claim 19, wherein the logic to implement one or more neurons in the SNN comprises a soma process and one or more dendrite processes for each neuron to be implemented by the respective neuromorphic core.