A spike timing dependent plasticity (STDP) rule is applied in a spiking neural network (SNN) that includes artificial synapses bi-directionally connecting artificial neurons in the SNN to model locations within a physical environment. A first neuron is activated to cause a spike wave to propagate from the first neuron to other neurons in the SNN. Propagation of the spike wave causes synaptic weights of a subset of the synapses to be increased based on the STDP rule. A second neuron is activated after propagation of the spike wave to cause a spike chain to propagate along a path from the second neuron to the first neuron, based on the changes to the synaptic weights. A physical path is determined from the second to the first neuron based on the spike chain, and a signal may be sent to a controller of an autonomous device to cause the autonomous to navigate the physical path.
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 spike timing dependent plasticity (STDP) rule to be applied in a spiking neural network (SNN), wherein the SNN comprises a plurality of artificial synapses to bi-directionally connect neurons in the plurality of neurons to model a plurality of locations within a physical environment; send a first signal to activate a first one of a plurality of artificial neurons, wherein the first signal is to designate a first location in the plurality of locations as a destination location, activating the first neuron causes a spike wave to propagate from the first neuron to other neurons in the plurality of neurons, propagation of the spike wave causes synaptic weights of a subset of the plurality of synapses to be increased based on the STDP rule, and the subset of synapses comprises synapses to transmit spikes in a direction opposed to the propagation of the spike wave; send a second signal to activate a second one of the plurality of artificial neurons, wherein the second signal is to be sent after propagation of the spike wave and is to designate a second location in the plurality of locations as a starting location, and activation of the second neuron causes a spike chain to propagate along a path from the second neuron to the first neuron; and send a third signal to a controller of an autonomous device to cause the autonomous to navigate a physical path from the starting location to the destination location based on the path. 2. The storage medium of claim 1, wherein each of the plurality of neurons corresponds to a respective one of the plurality of locations, the first neuron is defined to correspond to the first location, and the second neuron is defined to correspond to the second location. 3. The storage medium of claim 2, wherein the spike chain propagates to a subset of the plurality of neurons on a portion of the subset of synapses, and the path comprises a path over locations represented by the subset of neurons. 4. The storage medium of claim 1, wherein the SNN is defined to model a lattice network corresponding coordinates in the physical environment. 5. The storage medium of claim 4, wherein the lattice network comprises one of a two-dimensional lattice to model a two-dimensional physical space or a three-dimensional lattice to model a three-dimensional physical space. 6. The storage medium of claim 1, wherein the instructions, when executed, further cause a machine to send a neuromodulatory signal after propagation of the spike wave to cause an increase in a spiking threshold potential parameter of each of the plurality of neurons. 7. The storage medium of claim 1, wherein the instructions, when executed, further cause a machine to: send a fourth signal after the first and second signals, wherein the fourth signal is to activate a third one of the plurality of artificial neurons, the fourth signal is to designate a third location corresponding to a third location as a different starting location, and activation of the third neuron causes another spike chain to propagate along a particular path from the third neuron to the first neuron; and determine a path from the third location to the destination location based on the particular path. 8. A method comprising: defining a spike timing dependent plasticity (STDP) rule to be applied in a spiking neural network (SNN), wherein the SNN comprises a plurality of artificial synapses to bi-directionally connect neurons in the plurality of neurons to model a plurality of locations within a physical environment; sending a first signal to activate a first one of a plurality of artificial neurons, wherein the first signal is to designate a first location in the plurality of locations as a destination location, activating the first neuron causes a spike wave to propagate from the first neuron to other neurons in the plurality of neurons, propagation of the spike wave causes synaptic weights of a subset of the plurality of synapses to be increased based on the STDP rule, and the subset of synapses comprises synapses to transmit spikes in a direction opposed to the propagation of the spike wave; sending a second signal to activate a second one of the plurality of artificial neurons, wherein the second signal is to be sent after propagation of the spike wave and is to designate a second location in the plurality of locations as a starting location, and activation of the second neuron causes a spike chain to propagate along a path from the second neuron to the first neuron; and sending a third signal to a controller of an autonomous device to cause the autonomous to navigate a physical path from the starting location to the destination location based on the path. 9. 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 physical environment, and each neuron in the SNN models a respective one of a plurality of locations within the physical environment, wherein the neuromorphic computing device is to: route a first signal to activate a first one of a plurality of artificial neurons, wherein the first neuron corresponds to a first one of the plurality of locations; generate a first set of spikes to be sent by the first neuron in response to activation of the first neuron, wherein the first set of spikes causes a spike wave in the SNN; propagate the spike wave to other neurons in the plurality of neurons on artificial synapses in the SNN, wherein the artificial synapses are implemented using the routing fabric; adjust synaptic weights of a subset of the synapses based on propagation of the spike wave and a spike timing dependent plasticity (STDP) rule, and the subset of synapses comprises synapses to transmit spikes in a direction opposed to the propagation of the spike wave; 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 locations; generate a second set of spikes to be sent by the second neuron in response activation of the second neuron; propagate a spike chain across a portion of the subset of synapses, wherein the spike chain is initiated by the second set of spikes, and the spike chain is to propagate along a path defined by the portion of the subset of synapses from the second neuron to the first neuron in the SNN. 10. The apparatus of claim 9, wherein the neuromorphic computing device further comprises logic to generate data to describe the path, wherein the path comprises a subset of the neurons and corresponds to a path in the physical environment comprising locations modeled by the subset of neurons. 11. The apparatus of claim 9, wherein the neuromorphic computing device is further to implement at least one of the neurons in the SNN as a hazard neuron to model a hazard location in the physical environment. 12. The apparatus of claim 11, wherein the hazard location comprises an absolute hazard and the hazard neuron is implemented to be disconnected from other neurons in the SNN and is not to receive the spike wave. 13. The apparatus of claim 11, wherein the neuromorphic computing device implements the hazard neuron to cause the hazard neuron to delay sending of postsynaptic spikes responsive to presynaptic spikes to be received at the hazard neuron. 14. The apparatus of claim 13, wherein the delay is implemented by increasing an axonic delay parameter of the hazard neuron. 15. The apparatus of claim 13, wherein the delay is implemented by providing an inhibitory interneuron to correspond to the hazard neuron to model neural inhibition at the hazard neuron. 16. The apparatus of claim 13, wherein length of the delay corresponds to severity of the hazard location. 17. The apparatus of claim 9, wherein the first signal comprises a plurality of first signals and the plurality of first signals are to be routed to a set of destination neurons comprising the first neuron and at least a third neuron, and the spike wave is initiated by spikes generated at each of the neurons in the set of destination neurons. 18. The apparatus of claim 17, wherein a first portion of the spikes to initiate the spike wave are to be sent a duration of time ahead of a second portion of the spikes to initiate the spike wave to model relative desirability of a first portion of the set of destination neurons over a second portion of the set of destination neurons, wherein the first portion of the spikes are to be sent by the first portion of destination neurons, and the second portion of the spikes are to be sent by the second portion of the destination neurons. 19. The apparatus of claim 18, wherein a length of the duration of time corresponds to a degree of difference in the relative desirability. 20. The apparatus of claim 9, wherein the path comprises a SNN path and the apparatus further comprises: a particular device comprising a movement control to: accept an input based on the SNN path; and direct movement of the particular device along a physical path within the physical environment based on the input.