Single-shot learning and disambiguation of multiple predictions in hierarchical temporal memory is provided. In various embodiments an input sequence is read. The sequence comprises first, second, and third time-ordered components. Each of the time-ordered components is encoded in a sparse distributed representation. The sparse distributed representation of the first time-ordered component is inputted into a first portion of a hierarchical temporal memory. The sparse distributed representation of the second time-ordered component is inputted into a second portion of the hierarchical temporal memory. The second portion is connected to the first portion by a first plurality of synapses. A plurality of predictions as to the third time-ordered component is generated within a third portion of the hierarchical temporal memory. The third portion is connected to the second portion by a second plurality of synapses. Based on the plurality of predictions, additional synaptic connections are added between the first portion and the second portion.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: reading an input sequence, the sequence comprising first, second, and third time-ordered components; encoding each of the first, second, and third time-ordered components in a sparse distributed representation; inputting the sparse distributed representation of the first time-ordered component into a first portion of a hierarchical temporal memory; inputting the sparse distributed representation of the second time-ordered component into a second portion of the hierarchical temporal memory, the second portion being connected to the first portion by a first plurality of synapses; generating a plurality of predictions as to the third time-ordered component within a third portion of the hierarchical temporal memory, the third portion being connected to the second portion by a second plurality of synapses; and based on the plurality of predictions, adding additional synaptic connections between the first portion and the second portion. 2. The method of claim 1, wherein each of the first, second, and third portions comprises a plurality of columns, each of the columns comprising a plurality of cells. 3. The method of claim 1, comprising: disambiguating between each of the plurality of predictions. 4. The method of claim 1, wherein generating the plurality of predictions comprises: activating a plurality of cells in the third portion based on a plurality of cells active in the second portion. 5. The method of claim 2, wherein the plurality of cells in the first portion is connected to one or more aggregating segments, each of the one or more aggregating segments being connected to one of the plurality of cells in the second portion. 6. The method of claim 2, wherein the plurality of cells in the second portion is connected to one or more aggregating segments, each of the one or more aggregating segments being connected to one of the plurality of cells in the third portion. 7. The method of claim 4, wherein the plurality of cells active in the first portion encode the sparse distributed representation of the first time-ordered component. 8. The method of claim 4, wherein the plurality of cells active in the second portion encode a plurality of instances of the second time-ordered component. 9. The method of claim 4, wherein the plurality of cells active in the third portion encode the plurality of predictions. 10. The method of claim 9, wherein each of the plurality of cells active in the third portion comprises an instance identifier. 11. The method of claim 1, wherein the first, second, and third portions of the hierarchical temporal memory are all in the same region of the hierarchical temporal memory. 12. A system comprising a temporal memory, wherein: the temporal memory comprises first, second, and third portions; each portion comprises a plurality of columns; each column comprises a plurality of cells; the second portion is connected to the first portion by a first plurality of synapses; the third portion is connected to the second portion by a first plurality of synapses; a plurality of cells is active in the first portion, encoding a sparse distributed representation of a first time-ordered component; and a plurality of cells is active in the second portion, encoding a sparse distributed representation of a plurality of instances of the second time-ordered component, the temporal memory being adapted to: generate a plurality of predictions as to a third time-ordered component within the third portion, and based on the plurality of predictions, add additional synaptic connections between the first portion and the second portion. 13. The system of claim 12, wherein each of the first, second, and third portions comprise a plurality of columns, each column comprising a plurality of cells. 14. The system of claim 12, the temporal memory being adapted to: disambiguate between each of the plurality of predictions. 15. The system of claim 12, wherein generating the plurality of predictions comprises: activating a plurality of cells in the third portion based on a plurality of cells active in the second portion. 16. The system of claim 13, wherein the plurality of cells in the first portion is connected to one or more aggregating segments, each of the one or more aggregating segments being connected to one of the plurality of cells in the second portion. 17. The system of claim 13, wherein the plurality of cells in the second portion is connected to one or more aggregating segments, each of the one or more aggregating segments being connected to one of the plurality of cells in the third portion. 18. The system of claim 15, wherein the plurality of cells active in the first portion encode the sparse distributed representation of the first time-ordered component. 19. The system of claim 15, wherein the plurality of cells active in the second portion encode a plurality of instances of the second time-ordered component. 20. The system of claim 15, wherein the plurality of cells active in the third portion encode the plurality of predictions. 21. The system of claim 20, wherein each of the plurality of cells active in the third portion comprise an instance identifier. 22. The system of claim 12, wherein the first, second, and third portions of the hierarchical temporal memory are in the same region of the hierarchical temporal memory. 23. A computer program product for operating a temporal memory, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: reading an input sequence, the sequence comprising first, second, and third time-ordered components; encoding each of the first, second, and third time-ordered components in a sparse distributed representation; inputting the sparse distributed representation of the first time-ordered component into a first portion of a hierarchical temporal memory; inputting the sparse distributed representation of the second time-ordered component into a second portion of the hierarchical temporal memory, the second portion being connected to the first portion by a first plurality of synapses; generating a plurality of predictions as to the third time-ordered component within a third portion of the hierarchical temporal memory, the third portion being connected to the second portion by a second plurality of synapses; and based on the plurality of predictions, adding additional synaptic connections between the first portion and the second portion. 24. The computer program of claim 23, wherein the first, second, and third portions of the hierarchical temporal memory are in the same region of the hierarchical temporal memory. 25. A method comprising: reading a first time-ordered component of an input sequence; encoding the first time-ordered component in a sparse distributed representation; inputting the sparse distributed representation of the first time-ordered component into a region of a hierarchical temporal memory, the region being interconnected by a plurality of synapses; reading a second time-ordered component of the input sequence; encoding the second time-ordered component in a sparse distributed representation; inputting the sparse distributed representation of the second time-ordered component into the region of the hierarchical temporal memory generating a plurality of predictions as to a third time-ordered component of the input sequence within the region of the hierarchical temporal memory; and based on the plurality of predictions, adding additional synaptic connections in the region.