Embodiments relate to a first processing node that processes an input data having a temporal sequence of spatial patterns by retaining a higher-level context of the temporal sequence. The first processing node performs temporal processing based at least on feedback inputs received from a second processing node. The first processing node determines whether learned temporal sequences are included in the input data based on sequence inputs transmitted within the same level of a hierarchy of processing nodes and the feedback inputs received from an upper level of the hierarchy of processing nodes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving input data that change over time at a first processing node; generating a series of sparse distributed vectors over time based on the input data at the first processing node; performing temporal processing on the series of sparse distributed vectors at the first processing node by at least: generating sequence inputs varying over time based on the series of sparse distributed vectors, transmitting the sequence inputs via lateral connections within the first processing node, receiving feedback inputs from a second processing node via first feedback connections, and determining whether one or more learned temporal sequences of spatial patterns are included in the input data based on the transmitted sequence inputs and the received feedback inputs; and generating output data based on the temporal processing, the output data indicating at least prediction or detection of one or more spatial patterns or one or more temporal sequences in the input data. 2. The method of claim 1, generating the sequence inputs comprises selectively activating a subset of cells arranged in a plurality of columns, each of the cells connected to a subset of other cells via the lateral connections, wherein each of the cells transmit a sequence input to the subset of other cells via a lateral connection responsive to activation of each of the cells. 3. The method of claim 2, wherein each of the cells is activated based on at least one of (i) a sparse vector having an active element corresponding to a column to which each of the cell is assigned, (ii) first sequence inputs received by each of the cells from a predetermined number or portion of a set of lateral connections associated with each of the cells, and (iii) each of the cells in the first processing node receiving first feedback inputs through a predetermined number or portion of a first set of the first feedback connections, the output data indicating at least whether each of the cells or a column of each of the cells is activated. 4. The method of claim 3, wherein each of the cells is activated responsive to receiving the first feedback inputs through the predetermined number or portion of the first set of the first feedback connections and responsive to receiving second feedback inputs through a predetermined number or portion of a second set of the first feedback connections without receiving the first sequence inputs at each cell via the predetermined number or portion of the set of lateral connections associated with each of the cells. 5. The method of claim 4, wherein each of the cells is activated responsive to receiving the first feedback inputs and the second feedback inputs within a predetermined number of time steps or a predetermined time interval. 6. The method of claim 1, further comprising, responsive to determining that the one or more learned temporal sequences are included in the input data, establishing second feedback connections between the first processing node and the second processing node, the second feedback connections representing relationships between the one or more learned temporal sequences and one or more temporal sequences in the output data detected by the second processing node. 7. The method of claim 1, further comprising: assigning a permanence value to each of the first feedback connections; adjusting the permanence value depending on whether the one or more learned temporal sequences are included in the input data; and determining whether to retain or sever each of the first feedback connections based on the permanence value. 8. The method of claim 7, wherein adjusting the permanence value of each of the first feedback connections comprises: increasing the permanence value of each of the first feedback connections that contributes to correctly predicting that the one or more learned temporal sequences are included in the input data; and decreasing the permanence value of each of the first feedback connections that do not contribute to correctly predicting that the one or more learned temporal sequences are included in the input data, wherein one or more of the first feedback connections is severed responsive to permanence values associated with the one or more of the first feedback connections dropping below a threshold value. 9. The method of claim 8, responsive to severing the one or more first feedback connections, establishing second feedback connections based on detection of one or more learned temporal sequences in the input data. 10. The method of claim 1, further comprising: generating another series of sparse distributed vectors over time at the second processing node based on the output data received from the first processing node; and performing temporal processing on the other series of sparse distributed vectors at the second processing node by at least: generating another set of sequence inputs varying over time based on the other series of sparse distributed vectors, generating the feedback inputs based on the other set of sequence inputs, and determining whether one or more learned temporal sequences are included in the input data based at least on the other series of sequence inputs. 11. The method of claim 10, wherein performing the temporal processing at the second processing node comprises activating a subset of cells arranged by columns in the second processing nodes, the feedback inputs and the sequence inputs representing activation of a subset of cells in the second processing node. 12. A computing device comprising: a processor; and a memory operably coupled to the processor, the memory storing an instance of a first processing node, the first processing node comprising: a spatial pooler configured to receive input data that change over time, and generate a series of sparse distributed vectors over time based on the input data; and a sequence processor configured to: perform temporal processing on the series of sparse distributed vectors at the first processing node by at least generating sequence inputs varying over time based on the series of sparse distributed vectors, transmit sequence inputs via lateral connections within the first processing node, receive feedback inputs from a second processing node via first feedback connections, determine whether one or more learned temporal sequences of spatial patterns are included in the input data based on the transmitted sequence inputs and the received feedback inputs, and send output data to the second processing node for performing at least temporal processing on the output data, the output data indicating at least prediction or detection of one or more spatial patterns or one or more temporal sequences in the input data. 13. The computing device of claim 12, wherein the sequence processor is further configured to generate the sequence inputs comprises selectively activating a subset of cells arranged in a plurality of columns, each of the cells connected to a subset of other cells via the lateral connections, wherein each of the cells transmit a sequence input to the subset of other cells via a lateral connection responsive to activation of each of the cells. 14. The computing device of claim 13, wherein the sequence processor is further configured to activate each of the cells based on at least one of (i) a sparse vector having an active element corresponding to a column to which each of the cell is assigned, (ii) first sequence inputs received by each of the cells from a predetermined number or portion of a set of lateral connections associated with each of the cells, and (iii) each of the cells in the first processing node receiving first feedback inputs through a predetermined number or portion of a first set of the first feedback connections, the output data indicating at least whether each of the cells or a column of each of the cells is activated. 15. The computing device of claim 14, wherein the sequence processor is further configured to activate each of the cells responsive to receiving the first feedback inputs through the predetermined number or portion of the first set of the first feedback connections and responsive to receiving second feedback inputs through a predetermined number or portion of a second set of the first feedback connections without receiving the first sequence inputs via the predetermined number or portion of the set of lateral connections associated with each of the cells. 16. The computing device of claim 15, wherein each of the cells is activated responsive to receiving the first feedback inputs and the second feedback inputs within a predetermined number of time steps or a predetermined time interval. 17. The computing device of claim 12, wherein the sequence processor is further configured to establish second feedback connections between the first processing node and the second processing node responsive to determining that the one or more learned temporal sequences are included in the input data, the second feedback connections representing relationships between the one or more learned temporal sequences and one or more temporal sequences in the output data detected by the second processing node. 18. The computing device of claim 12, wherein the sequence processor is further configured to: assign a permanence value to each of the first feedback connections; adjust the permanence value depending on whether the one or more learned temporal sequences are included in the input data; and determine whether to retain or sever each of the first feedback connections based on the permanence value. 19. The computing device of claim 18, wherein the sequence processor is further configured to: increase the permanence value of each of the first feedback connections that contributes to correctly predicting that the one or more learned temporal sequences are included in the input data; and decrease the permanence value of each of the first feedback connections that do not contribute to correctly predicting that the one or more learned temporal sequences are included in the input data, wherein one or more of the first feedback connections is severed responsive to permanence values associated with the one or more of the first feedback connections dropping below a threshold value. 20. The computing device of claim 19, wherein the sequence processor is further configured to establish second feedback connections based on detection of one or more learned temporal sequences in the input data responsive to severing the one or more first feedback connections. 21. The computing device of claim 12, wherein the second processing node comprises: an upper spatial pooler configured to generate another series of sparse distributed vectors over time based on the output data received from the first processing node; and an upper sequence processor configured to perform temporal processing on the other series of sparse distributed vectors at the second processing node by at least: generating another set of sequence inputs varying over time based on the other series of sparse distributed vectors, generating the feedback inputs based on the other set of sequence inputs, and determining whether one or more learned temporal sequences are included in the input data based at least on the other series of sequence inputs. 22. The computing device of claim 21, wherein the upper sequence processor is configured to perform the temporal processing at the second processing node by at least activating a subset of cells arranged by columns in the second processing nodes, the feedback inputs and the sequence inputs representing activation of the subset of cells in the second processing node. 23. A non-transitory computer readable storage medium comprising instructions when executed by a processor cause the processor to instantiate a first processing node, the first processing node configured to: receive input data that change over time at the first processing node; generate a series of sparse distributed vectors over time based on the input data at the first processing node; perform temporal processing on the series of sparse distributed vectors at the first processing node by at least: generating sequence inputs varying over time based on the series of sparse distributed vectors, transmitting sequence inputs via lateral connections within the first processing node, receiving feedback inputs from a second processing node via first feedback connections, and determining whether one or more learned temporal sequences of spatial patterns are included in the input data based on the transmitted sequence inputs and the received feedback inputs; and send output data to the second processing node for performing at least temporal processing on the output data, the output data indicating at least prediction or detection of one or more spatial patterns or one or more temporal sequences in the input data.