A neuromorphic computing apparatus has a network of neuromorphic cores, with each core including an input axon and a plurality of neurons having synapses. The input axon is associated with an input data store to store an input trace representing a time series of filtered pre-synaptic spike events, and accessible by the synapses of the plurality of neurons of the core. Each neuron includes at least one dendritic compartment to store and process variables representing a dynamic state of the neuron. Each compartment is associated with a compartment-specific data store to store an output trace representing a time series of filtered post-synaptic spike events. Each neuron includes a learning engine to apply a set of one or more learning rules based on the pre-synaptic and post-synaptic spike events to produce an adjustment of parameters of a corresponding synapse to those spike events.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neuromorphic computing apparatus comprising: computing hardware, including processing and data storage circuitry; a network of spiking neural network cores implemented in the computing hardware, each core including an input axon and a plurality of neurons, including synapses, wherein the input axon is common to plurality of neurons; wherein the input axon is associated with an input data store to store an input trace representing a time series of pre-synaptic spike events that are filtered according to a filtering function, and accessible by the synapses of the plurality of neurons of the core; wherein each one of the plurality of neurons includes at least one dendritic compartment to store and process variables representing a dynamic state of the neuron, and wherein the at least one dendritic compartment is associated with a compartment-specific data store to store an output trace representing a time series of post-synaptic spike events that are filtered according to a filtering function; and wherein each one of the plurality of neurons includes a learning engine to apply a set of one or more learning rules based on the pre-synaptic and post-synaptic spike events to produce an adjustment of parameters of a corresponding synapse to those spike events. 2. The apparatus of claim 1, wherein the input data store is to store a plurality of input traces. 3. The apparatus of claim 1, wherein the compartment-specific data store associated with each of the at least one compartment is to store a plurality of output traces. 4. The apparatus of claim 1, wherein the input axon is a dedicated reward axon responsive to reinforcement-learning signaling, and wherein the learning engine is to read, process, and adjust, the reinforcement-learning signaling according to applicable learning rules. 5. The apparatus of claim 1, wherein the input axon is associated with a reinforcement-learning data store that stores at least one reinforcement-learning trace representing a temporal sequence of reinforcement-learning state variables. 6. The apparatus of claim 1, wherein the adjustment of the parameters of the corresponding synapse includes at least one adjustment selected from among: weighting adjustment, delay adjustment tag adjustment, or any combination thereof. 7. The apparatus of claim 1, wherein the learning engine is to access a forward-mapping data structure that associates fan-outs of the input axon with dendritic compartments that are responsive to stimuli arriving on the input axon. 8. The apparatus of claim 1, wherein the learning engine is to access a backward-mapping data structure that associates each dendritic compartment to a corresponding set of fan-in input axons. 9. The apparatus of claim 1, wherein the learning engine is to perform a learning cycle in response to a predefined passage of time steps, wherein the predefined passage of time steps represents a learning epoch. 10. The apparatus of claim 9, wherein the input data store maintains historical values of the input trace, including time offsets of the pre-synaptic spike events, over a set of prior epochs, to facilitate historic reconstruction of the input trace. 11. The apparatus of claim 10, wherein the historical values of the input trace are used to compute trace values for a current epoch at fanout synapses with nonzero network delay. 12. The apparatus of claim 9, wherein the input data store and each trace-specific data store update their stored traces only once per learning epoch except to record any spike event occurrences within the learning epoch. 13. The apparatus of claim 9, wherein the learning engine applies learning rules unconditionally in response to passage of a predefined number of learning epochs. 14. The apparatus of claim 1, wherein the learning engine is programmable to access a plurality of different learning rule profiles. 15. The apparatus of claim 1, wherein the learning engine is programmable to apply a plurality of learning rules. 16. The apparatus of claim 1, wherein the learning engine is to compare the pre-synaptic or post-synaptic state variables against a learning exit condition, wherein upon satisfaction of the learning exit condition, the learning engine is to terminate a learning operation. 17. The apparatus of claim 1, wherein the learning engine is to apply learning rules that are expressed as a sum-of-products semantic, with each product comprising a programmable series of trace values, synaptic parameters, and constants available to the learning engine. 18. The apparatus of claim 1, wherein each neuron includes: an input to accept pre-synaptic signaling from an input axon associated with the neuromorphic core; a synapse to store and distribute input and feedback signaling for processing, the synapse including a set of pre-synaptic terminals; and an output to carry post-synaptic signaling from the neuron to other neurons. 19. A method for operating a neuromorphic computing system, the method being executed by computing hardware, and comprising: configuring a network of spiking neural network cores in the computing hardware, each core including an input axon and a plurality of neurons, including synapses, wherein the input axon is common to plurality of neurons; associating the input axon with an input data store to store an input trace representing a Lime series of pre-synaptic spike events that are filtered according to a filtering function, and accessible by the synapses of the plurality of neurons of the core; providing each one of the plurality of neurons with at least one dendritic compartment to store and process variables representing a dynamic state of the neuron; associating the at least one dendritic compartment with a compartment-specific data store to store an output trace representing a time series of post-synaptic spike events that are filtered according to a filtering function; and for each one of the plurality of neurons, applying a set of one or more learning rules based on the pre-synaptic and post-synaptic spike events to produce an adjustment of parameters of a corresponding synapse to those spike events. 20. The method of claim 19, wherein the input axon is a dedicated reward axon responsive to reinforcement-learning signaling, and further comprising: reading, processing, and adjusting, the reinforcement-learning signaling according to applicable learning rules. 21. The method of claim 19, wherein the input axon is associated with a reinforcement-learning data store that stores at least one reinforcement-learning trace representing a temporal sequence of reinforcement-learning state variables. 22. A at least one machine-readable medium containing instructions that, when executed by computing hardware, cause the computing hardware to perform: configuring a network of spiking neural network cores in the computing hardware, each core including an input axon and a plurality of neurons, including synapses, wherein the input axon is common to plurality of neurons; associating the input axon with an input data store to store an input trace representing a time series of pre-synaptic spike events that are filtered according to a filtering function, and accessible by the synapses of the plurality of neurons of the core; providing each one of the plurality of neurons with at least one dendritic compartment to store and process variables representing a dynamic state of the neuron; associating the at least one dendritic compartment with a compartment-specific data store to store an output trace representing a time series of post-synaptic spike events that are filtered according to a filtering function; and for each one of the plurality of neurons, applying a set of one or more learning rules based on the pre-synaptic and post-synaptic spike events to produce an adjustment of parameters of a corresponding synapse to those spike events. 23. The at least one machine-readable medium of claim 22, further comprising instructions for performing a learning cycle in response to a predefined passage of time steps, wherein the predefined passage of time steps represents a learning epoch. 24. The at least one machine-readable medium of claim 23, wherein the input data store maintains historical values of the input trace, including time offsets of the pre-synaptic spike events, over a set of prior epochs, to facilitate historic reconstruction of the input trace. 25. The at least one machine-readable medium of claim 23, further comprising instructions for applying learning rules unconditionally in response to passage of a predefined number of learning epochs.