Abstract: Described is a system for feature selection that extends supervised hierarchical clustering to neural activity signals. The system generates, using a hierarchical clustering process, a hierarchical dendrogram representing a set of neural activity data comprising individual neural data elements having neural activity patterns. The hierarchical dendrogram is searched for an optimal cluster parcellation using a stochastic supervised search process. An optimal cluster parcellation of the hierarchical dendrogram is determined that provides a classification of the set of neural activity data with respect to a supervised classifier, resulting in a reduced neural activity feature set. The set of neural activity data is classified using the reduced neural activity feature set, and the classified set of neural activity data is decoded.

Abstract: Described is a system for selecting among intelligence elements of a neural model. An intelligence element is selected from a set of intelligence elements which change group attack probability estimates and processed via multiple operations. A semantic memory component learns group probability distributions and rules based on the group probability distributions. The rules determine which intelligence element related to the groups to select. Given an environment of new probability distributions, the semantic memory component recalls which rule to select to receive a particular intelligence element. An episodic memory component recalls a utility value for each information element A procedural memory component recalls and selects the information element considered to have the highest utility. A list of intelligence elements is published to disambiguate likely attackers.

Abstract: Described is a system for neural decoding of neural activity. Using at least one neural feature extraction method, neural data that is correlated with a set of behavioral data is transformed into sparse neural representations. Semantic features are extracted from a set of semantic data. Using a combination of distinct classification modes, the set of semantic data is mapped to the sparse neural representations, and new input neural data can be interpreted.

Abstract: Described is a system for proactive and reactive cognitive control using a neural module. The system calculates, for each hypothesis of a set of hypotheses, a probability that an event will occur. The neural module comprises a plurality of neurons and includes the PC module, a prefrontal cortex (PFC) module, an anterior cingulate cortex (ACC) module, a locus coeruleus (LC) module, and a basal forebrain (BF) module. The set of hypotheses are related to tasks to be performed by a plurality of groups, each group having a corresponding hypothesis. For each probability, the system calculates a conflict value across all hypotheses with the ACC module, compares each conflict value to a predetermined threshold using the BF and LC modules. A determination is made whether to directly output the calculated probability or perform an additional probability calculation and output an updated probability.

Abstract: Described is a system for explaining how the human brain represents conceptual knowledge. A semantic model is developed, and a behavioral exam is performed to assess a calibration subject into a cohort and reveal semantic relationships to modify a personalized semantic space developed by the semantic model. Semantic features are extracted from the personalized semantic space. Neural features are extracted from neuroimaging of the human subject. A neuroceptual lattice is created having nodes representing attributes by aligning the semantic features and the neural features. Structures in the neuroceptual lattice are identified to quantify an extent to which the set of neural features represents a target concept. The identified structures are used to interpret conceptual knowledge in the brain of a test subject.

Abstract: Described is a system for generating a semantic space based on the lexical relations between words. The system determines synonym and antonym relations between a set of words. A lexical graph is generated based on the synonym and antonym relations. Manifold embedding of the lexical graph is determined, and Laplacian coordinates of the manifold embedding are assigned as semantic features of the set of words. A quantitative representation of the set of words is generated using the semantic features.

Abstract: The system classifies data using formal concept analysis (FCA). In a training phase, the system generates a FCA classification lattice, having a structure, using a set of training data. The set of training data comprises training presentations and classifications corresponding to the training presentations. In a classification phase, a set of test data having classes that are hierarchical in nature is classified using the structure of the FCA classification lattice.

Abstract: Described is system for automatically detecting network services and their dependencies. The system generates a first context table having rows of packet headers and columns of header field values. A first concept lattice is generated from the first context table, and network services and corresponding packet headers are identified. A second context table is generated using the networks services data, and a second concept lattice is generated from the second context table. Network service dependencies are identified using the second concept lattice. The context tables are used to monitor the plurality of network service dependencies.

Abstract: Described is system and method for cognitive recognition. The system receives a multi-dimensional scene array as input data. A foveation module divides the multi-dimensional scene array into a plurality of sub-arrays and outputs contents of a currently selected sub-array. The contents are clustered with a hierarchical clustering module to generate a spatially invariant hierarchical cluster of the contents comprising a plurality of components which are based on a statistical distribution of co-occurrence of features across the currently selected sub-array. Task-relevant components are selectively gated and robustly maintained into a component memory location of a pattern bank with a working memory module with an input gating module. If the task-relevant components activate an abstract category module based on pattern matching, then a category recognition label is generated for the contents of the currently selected sub-array with an executive control module.

Abstract: Described is system for feature transformation of neural activity using sparse and low-rank (SLR) decomposition. A set of neural activity signals associated with different stimuli are obtained, and a neural feature is extracted or each stimuli from the set of neural activity signals using SLR decomposition. The neural feature is then used to generate a classification of the stimuli. The neural activity signals may include functional magnetic resonance imaging (fMRI) signals, fMRI blood-oxygen-level dependent (BOLD) signals, electroencephalography (EEG) signals, functional near-infrared spectroscopy (fNIRS) signals, or magnetoencephalography (MEG) signals. The system according to the principles of the present invention will he an important component of any neural activity based classification system.

Abstract: Described is a system for feature selection for formal concept analysis (FCA). A set of data points having features is separated into object classes. For each object class, the data points are convolved with a Gaussian function, resulting in a class distribution curve for each known object class. For each class distribution curve, a binary array is generated having ones on intervals of data values on which the class distribution curve is maximum with respect to all other class distribution curves, and zeroes elsewhere. For each object class, a binary class curve indicating for which interval a performance of the known object class exceeds all other known object classes is generated. The intervals are ranked with respect to a predetermined confidence threshold value. The ranking of the intervals is used to select which features to extract from the set of data points in FCA lattice construction.

Abstract: Described is system for data classification using formal concept analysis (FCA). In a training phase, the system generates a FCA classification lattice, having a structure, using a set of training data. The set of training data comprises training presentations and classifications corresponding to the training presentations. In a classification phase, a set of test data having classes that are hierarchical in nature is classified using the structure of the FCA classification lattice.

Abstract: Described is system for extracting conceptual knowledge representation from neural system. The system extracts a first set of attributes to define a set of objects in a first domain. A first formal concept lattice is constructed comprising the set of objects and the first set of attributes from the first domain. A second set of attributes is extracted to define the set of objects in a second domain. A second formal concept lattice is constructed comprising the set of objects and the second set of attributes from the second domain. The first formal concept lattice is aligned with the second formal concept lattice to link the first set of attributes with the second set of attributes, wherein a combined lattice is generated. The combined lattice is used to relate the first domain to the second domain.