George Mason Intellectual Properties, Inc.

Hrl Laboratories, LLC

Giorgio A Ascoli

Ruggero Scorcioni

Rajan Bhattacharyya

Michael D Howard

Suhas E Chelian

James Benvenuto

Sridevi Polavaram

Bonnie Lasher

Alexei Samsonovich

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.

Method and apparatus for decision making in a neural model including semantic, episodic, and procedural memory components

A method and system comprising: generating at least one slice image by carpeting a multitude of digital images based upon a two-dimensional (2-D) reference system, each of the multitude of digital images associated with a location in the 2-D reference system and each showing a representation of an arborization and/or landmark such that digital images not showing a representation of an arborization and/or landmark are excluded; carpeting the multitude of digital images together to create a slice image; aligning the slice image with other similarly created slice images using landmark information to determine the amount of 2-D rotation and/or translation to be applied to each slice image; vectorizing the representation of the arborization on each slice image, the vectorization creating a series of segments; assigning a type to at least one of the series of segments based on a unique characteristic of arborization; and grouping related segments together.

Arborization reconstruction

A semantic cognitive map created by associating each of a multitude of dictionary entries with a point among a multitude of points in a metric space, each of the dictionary entries associated with at least one onym, the at least one onym including at least one synonym or antonym, the metric space having a topology and metrics, the location of each of the multitude of points defined by a global minimum of an energy function of the multitude of points.

Semantic cognitive map

A neuronal measurement tool including: an input module, a grouping module, a metric selection module, a statistical test selection module, a raw measurement module, a clustering module and a statistical test module. The input module inputs digitally reconstructed neuronal morphologies. The grouping module groups the digitally reconstructed neuronal morphologies into groups. The metric selection module selects at least one metric of interest. The statistical test selection module selects a statistical test method. The raw measurement module gathers raw measurements associated with the metric(s) of interest on the digitally reconstructed neuronal morphologies. The clustering module clusters the raw measurements into groups and the statistical test module performs the statistical test method between the raw measurements clustered in the groups.

Neuronal measurement tool

Disclosed is a digital neural processor comprising at least one neural processing element. The neural processing elements including at least one simulated dendrite and a simulated axon. Each of the simulated dendrites may include: a dendrite input capable of receiving at least one dendrite input signal and a dendrite signal propagation function capable of calculating a dendrite output signal in discrete time steps from each dendrite input signal. The signal propagation function may further include a delay parameter; a duration parameter; and an amplitude parameter. The simulated axon includes an axon input capable of receiving dendrite output signals, an axon function, capable of calculating an axon output signal from dendrite output signal(s) and an axon output capable of outputting the axon output signal.

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Fairfax Station, VA, United States of America

Giorgio A Ascoli