Embodiments relate to performing inference, such as object recognition, based on sensory inputs received from sensors and location information associated with the sensory inputs. The sensory inputs describe one or more features of the objects. The location information describes known or potential locations of the sensors generating the sensory inputs. An inference system learns representations of objects by characterizing a plurality of feature-location representations of the objects, and then performs inference by identifying or updating candidate objects consistent with feature-location representations observed from the sensory input data and location information. In one instance, the inference system learns representations of objects for each sensor. The set of candidate objects for each sensor is updated to those consistent with candidate objects for other sensors, as well as the observed feature-location representations for the sensor.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of performing inference, comprising: generating, by a first input processor, a first input representation indicating potential of a first pair of a first location and a first feature at the first location being detected; determining, by a first output processor, a first output representation corresponding to the first input representation, the first output representation indicating one or more candidates likely to be associated with the first pair; generating, by the first input processor, a second input representation indicating potential of a second pair of a second location and a second feature at the second location being detected subsequent to generating the first input representation; and determining, by the first output processor, a second output representation corresponding to the first input representation and the second input representation, the second output representation indicating one or more candidates likely to be associated with the first pair and the second pair. 2. The computer-implemented method of claim 1, further comprising generating, by the first output processor, a feedback signal representing a prediction on the second input representation based on the first output representation, wherein the second input representation is generated by the first input processor based further on the feedback signal. 3. The computer-implemented method of claim 1, further comprising: maintaining, by the first output processor, an output representation corresponding to a cause active during a training; generating, by the first input processor, training input representations for pairs of locations and corresponding features associated with the cause for the training; and associating the training input representations with the output representation corresponding to the cause. 4. The computer-implemented method of claim 1, wherein the first input representation is an activation state of a first subset of input cells in the first input processor and the second input representation is an activation state of a second subset of the input cells in the first input processor. 5. The computer-implemented method of claim 1, wherein the first location is one of a plurality of potential locations in the one or more candidates likely to be associated with the first pair. 6. The computer-implemented method of claim 5, wherein the first input representation indicates an activation of a union of pairs of the first feature and each of the potential locations. 7. The computer-implemented method of claim 6, wherein the first output representation further indicates one or more candidates likely associated with the union of pairs of the first feature and each of the potential locations. 8. The computer-implemented method of claim 7, wherein the second location is one of another plurality of potential locations generated by shifting each potential location in the plurality of potential locations by a predetermined distance. 9. The computer-implemented method of claim 8, wherein the second input representation indicates an activation of a union of pairs of the second feature and one or more of the potential locations in the other plurality of potential locations that are associated with the one or more candidates indicated by the first output representation. 10. The computer-implemented method of claim 4, wherein generating the second input representation comprises activating the second subsets of the input cells in the first input processor responsive to the second subsets of input cells receiving location signals representing the second location and activation signals associated with the second feature. 11. The computer-implemented method of claim 4, wherein the second subset of the input cells in the first input processor are activated based further on a feedback signal associated with the first output representation. 12. The computer-implemented method of claim 11, wherein each of the one or more candidates is represented by a corresponding activation state of a subset of output cells in the first output processor, and the feedback signal indicates the activation state of the subset of output cells. 13. The computer-implemented method of claim 1, wherein the second output representation is an activation state of a subset of output cells in the first output processor. 14. The computer-implemented method of claim 13, wherein generating the second output representation comprises activating the subset of the output cells in the first output processor responsive to the subset of output cells receiving feedforward signals corresponding to the second input representation. 15. The computer-implemented method of claim 14, wherein the feedforward signals are sent from activated subset of input cells in the first input processor associated with the second input representation to the subset of the output cells in the first output processor connected to the activated subset of the input cells. 16. The computer-implemented method of claim 4, further comprising: receiving first location information indicating the first location in a sparse distributed representation, the first input representation generated based on the first location information; and receiving second location information indicating the second location in a sparse distributed representation, the second input representation generated based on the second location information. 17. The computer-implemented method of claim 13, further comprising: receiving, by the first input processor, a first sensory input indicating the first feature from a sensor, the first input representation generated based on the first sensory input; and receiving, by the first input processor, a second sensory input indicating the second feature from the sensor, the second input representation generated based on the second sensory input. 18. The computer-implemented method of claim 17, further comprising: receiving, by a second input processor, a third sensory input indicating a third feature from another sensor; generating, by the second input processor, a third input representation of a third pair of the third feature and a third location corresponding to the third feature; determining, by a second output processor, activation states of a subset of other output cells in the second output processor corresponding to the third input representation, the activation states of the subset of the other output cells indicating one or more candidates likely to be associated with the third pair; and sending the activation states of the subset of the other output cells in the second output processor to one or more of the output cells in the first output processor connected to the other output cells, activation of the one or more of the output cells in the first output processor based further on the activation states of the subset of the other output cells in the second output processor. 19. The computer-implemented method of claim 1, further comprising: generating, by a second input processor, a third input representation of a third pair of a third location and a third feature at the third location; determining, by a second output processor, a third output representation corresponding to the third input representation, the third output representation indicating one or more candidates likely to be associated with the third pair; and sending the third output representation from the second output processor to the first output processor for determining the second output representation. 20. The computer-implemented method of claim 1, wherein the one or more candidates are objects, and the first and second locations are allocentric locations of candidate objects. 21. A computer-implemented method of performing inference, comprising: generating, by a first input processor, a first input representation indicating potential of a first pair of a first location and a first feature at the first location being detected; determining, by a first output processor, a first output representation corresponding to the first input representation, the first output representation indicating one or more candidates likely to be associated with the first pair; generating, by a second input processor, a second input representation indicating potential of a second pair of a second location and a second feature at the second location being detected; and determining, by a second output processor, a second output representation corresponding to the first output representation and the second input representation, the second output representation indicating one or more candidates likely to be associated with the first pair and the second pair. 22. The computer-implemented method of claim 21, further comprising determining, by the first output processor, an updated first output representation based on the first output representation and the second output representation, the second output representation indicating one or more candidates likely to be associated with the first pair and the second pair 23. The computer-implemented method of claim 21, further comprising generating, by the first output processor, a feedback signal representing a prediction on a subsequent input representation to be detected in the first input processor, wherein the subsequent input representation is generated by the first input processor based further on the feedback signal. 24. The computer-implemented method of claim 21, wherein the first input representation is an activation state of a first subset of input cells in the first input processor and the second input representation is an activation state of a second subset of the input cells in the second input processor. 25. The computer-implemented method of claim 21, wherein the first location is one of a plurality of potential locations in the one or more candidates likely to be associated with the first pair. 26. The computer-implemented method of claim 21, wherein the one or more candidates are objects, and the first and second locations are allocentric locations of candidate objects. 27. A non-transitory computer readable storage medium comprising instructions when executed by a processor cause the processor to instantiate a method of performing inference, comprising: generating, by a first input processor, a first input representation indicating potential of a first pair of a first location and a first feature at the first location being detected; determining by a first output processor, a first output representation corresponding to the first input representation, the first output representation indicating one or more candidates likely to be associated with the first pair; generating, by the first input processor, a second input representation indicating potential of a second pair of a second location and a second feature at the second location subsequent to generating the first input representation; and determining, by the first output processor, a second output representation corresponding to the first input representation and the second input representation, the second output representation indicating one or more candidates likely to be associated with the first pair and the second pair. 28. A computing device, comprising: a first input circuit configured to: generate a first input representation of a first pair of a first location and a first feature at the first location, and generate a second input representation of a second pair of a second location and a second feature at the second location; and a first output circuit coupled to the first input circuit and configured to: determine a first output representation corresponding to the first input representation, the first output representation indicating one or more candidates likely to be associated with the first pair, and determine a second output representation corresponding to the first input representation and the second input representation, the second output representation indicating one or more candidates likely to be associated with the first pair and the second pair. 29. The computing device of claim 28, further comprising: a second input circuit configured to generate a third input representation of a third pair of a third location and a third feature at the third location; and a second output circuit coupled to the second input circuit and the first output circuit, the second output circuit configured to: determine a third output representation corresponding to the third input representation, the third output representation indicating one or more candidates likely to be associated with the third pair, and send the third output representation to the first output circuit for determining the second representation. 30. The computing device, comprising: a first input circuit configured to generate a first input representation of a first pair of a first location and a first feature at the first location; a first output circuit coupled to the first input circuit and configured to determine a first output representation corresponding to the first input representation, the first output representation indicating one or more candidates likely to be associated with the first pair; a second input circuit configured to generate a generate a second input representation of the second pair; and a second output circuit coupled to the second input circuit and the first output circuit, the second output circuit configured to determine a second output representation corresponding to the first output representation and the second input representation, the second output representation indicating one or more candidates likely to be associated with the first pair and the second pair.