A method for inferring patterns in multi-dimensional image data comprises providing a recursive network of sub-networks with a parent feature node and at least two child feature nodes; wherein each sub-network is associated with a distinct subset of the space; configuring nodes of the sub-networks with posterior distribution component; receiving image data feature input at the final child feature nodes; propagating node activation through the network layer hierarchy in a manner consistent with node connections of sub-networks of the network and the posterior prediction of child nodes; and outputting parent feature node selection to an inferred output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for inferring patterns in image data, the data having a space of two or more dimensions, with a network comprising: providing a recursive network of sub-networks with a parent feature node and at least two child feature nodes; wherein each sub-network is associated with a distinct subset of the space; configuring nodes of the sub-networks with posterior distribution component; receiving image data feature input at the final child feature nodes; propagating node activation through the network layer hierarchy in a manner consistent with node connections of sub-networks of the network and the posterior prediction of child nodes; and outputting parent feature node selection to an inferred output. 2. The method of claim 1, wherein the distinct subsets of the space are non-overlapping. 3. The method of claim 1, further comprising enforcing a selection constraint between at least two nodes of the network. 4. The method of claim 3, wherein the selection constraint is enforced by a lateral constraint node between the two nodes; wherein the two nodes are in a same sub-network. 5. The method of claim 3, wherein the selection constraint is enforced by an external constraint node between the two nodes; wherein the two nodes are in different sub-networks. 6. The method of claim 3, wherein the network contains at least two sets of sub-networks; the first set of sub-networks associated with a first time and the second set of the sub-networks associated with a second time; wherein the selection constraint is enforced by a temporal constraint node between the two nodes; wherein the two nodes are in sub-networks of different sets. 7. A method for generating image data, the data having a space of two or more dimensions, with a network comprising: providing a recursive network of sub-networks with a parent feature input node and at least two child feature output nodes; wherein each sub-network is associated with a distinct subset of the space; propagating node selection down the network layer hierarchy in a manner consistent with node connections of sub-networks of the network, the propagation within the sub-network comprising: at an activated parent feature node of a sub-network, selecting a pool node consistent with a selection function of the parent feature node; and at a pool node of a sub-network, selecting at least a first parent-specific child feature (PSCF) node that corresponds to a child feature node of the sub-network; and compiling the state of final child feature nodes of the network into a generated image data output. 8. The method of claim 7, wherein the distinct subsets of the space are non-overlapping. 9. The method of claim 7, further comprising in response to the selection of a first node of the sub-network, enforcing a selection constraint on at least a second node of a second pool. 10. The method of claim 9, wherein the selection constraint is enforced by a lateral constraint node between the first and second nodes; wherein the first and second nodes are in a same sub-network. 11. The method of claim 9, wherein the selection constraint is enforced by a lateral constraint node between the first and second nodes; wherein the first and second nodes are in different sub-networks. 12. The method of claim 9, wherein the network contains at least two sets of sub-networks; the first set of sub-networks associated with a first time and the second set of the sub-networks associated with a second time; wherein the selection constraint is enforced by a temporal constraint node between the first and second nodes; wherein the first and second nodes are in sub-networks of different sets. 13. A system for processing image data, the data having a space of two or more dimensions, comprising: a recursively architected network of sub-networks organized into a plurality of hierarchical layers; wherein each sub-network is associated with a distinct subset of the space; the sub-networks comprising at least a parent feature node, a pool node, a parent-specific child feature (PSCF) node, and a child feature node; the parent feature node of at least one sub-network configured with a selection function actionable on at least two pool nodes connected to the parent feature node of the at least one sub-network; the pool node of the at least one sub-network configured with a selection function actionable on at least two PSCF nodes connected to the pool node of the at least one sub-network; the PSCF node of the at least one sub-network configured to activate a connected child feature node; and the child feature node connectable to at least a parent feature node of a sub-network at a lower hierarchical layer. 14. The system of claim 13, wherein the distinct subsets of the space are non-overlapping. 15. The system of claim 13, further comprising a constraint node with at least two connections from at least two PSCF nodes, with a selection function to augment selection by the pool node. 16. The system of claim 15, wherein the constraint node is a lateral constraint node between two PSCF nodes of a same sub-network. 17. The system of claim 15, wherein the constraint node is an external constraint node between two PSCF nodes of different sub-networks. 18. The system of claim 15, wherein the constraint node is a temporal constraint node between two PSCF nodes of a different sub-networks; the different sub-networks corresponding to different temporal ranges but to a same spatial range.