Applied Brain Research Inc.

Terrence Charles Stewart

Christopher David Eliasmith

Travis Crncich-DeWolf

Yichuan Tang

Trevor William Bekolay

Feng-Xuan Choo

Daniel Halden Rasmussen

Jean-Jacques Emile Slotine

Aaron Russell Voelker

Brent Komer

Travis Crncich-Dewolf

Daniel Haiden Rasmussen

The present invention relates to methods and systems for encoding and processing representations that include continuous structures using vector-symbolic representations. The system is comprised of a plurality of binding subsystems that implement a fractional binding operation, a plurality of unbinding subsystems that implement a fractional unbinding operation, and at least one input symbol representation that propagates activity through a binding subsystem and an unbinding subsystem to produce a high-dimensional vector representation of a continuous space.

Methods and systems for encoding and processing vector-symbolic representations of continuous spaces

Methods, systems and apparatus that provide for perceptual, cognitive, and motor behaviors in an integrated system implemented using neural architectures. Components of the system communicate using artificial neurons that implement neural networks. The connections between these networks form representations&#x2014;referred to as semantic pointers&#x2014;which model the various firing patterns of biological neural network connections. Semantic pointers can be thought of as elements of a neural vector space, and can implement a form of abstraction level filtering or compression, in which high-dimensional structures can be abstracted one or more times thereby reducing the number of dimensions needed to represent a particular structure.

Methods and systems for artificial cognition

Methods, systems and methods for designing a system that provides adaptive control and adaptive predictive filtering using nonlinear components. A system design is described that provides an engineered architecture. This architecture defines a core set of network dynamics that carry out specific functions related to control or prediction. The adaptation systems and methods can be applied to limited areas of the system to allow the system to learn to compensate for unmodeled system dynamics and kinematics. Two types of adaptive modules are described which are configured to account for the unmodeled system dynamics and kinematics.

Methods and systems for nonlinear adaptive control and filtering

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Waterloo, Canada

Terrence Charles Stewart