Abstract: A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

Abstract: A method for establishing sensorimotor programs includes specifying a concept relationship that relates a first concept to a second concept and establishes the second concept as higher-order than the first concept; training a first sensorimotor program to accomplish the first concept using a set of primitive actions; and training a second sensorimotor program to accomplish the second concept using the first sensorimotor program and the set of primitive actions.

Abstract: A method for establishing sensorimotor programs includes specifying a concept relationship that relates a first concept to a second concept and establishes the second concept as higher-order than the first concept; training a first sensorimotor program to accomplish the first concept using a set of primitive actions; and training a second sensorimotor program to accomplish the second concept using the first sensorimotor program and the set of primitive actions.

Abstract: A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

Abstract: A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

Abstract: A method for establishing sensorimotor programs includes specifying a concept relationship that relates a first concept to a second concept and establishes the second concept as higher-order than the first concept; training a first sensorimotor program to accomplish the first concept using a set of primitive actions; and training a second sensorimotor program to accomplish the second concept using the first sensorimotor program and the set of primitive actions.

Abstract: A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

Abstract: A system and method for generating and inferring patterns with a network that includes providing a network of recursive sub-networks with a parent feature input node and at least two child feature output nodes; propagating node selection through the network layer hierarchy in a manner consistent with node connections of sub-networks of the network, the propagation within the sub-network including enforcing a selection constraint on at least a second node of a second pool according to a constraint node of the sub-network; and compiling the state of final child feature nodes of the network into a generated output.

Abstract: A computer-implemented method for object recognition using a recursive cortical network comprising receiving an input image at an input module, applying a trained recursive cortical network (RCN) to the image using an inference module to activate child features of the RCN, selecting pools of the RCN containing the activated child features, propagating the selection of the pools to identify probabilities of one or more high-level features matching one or more objects in the input image.