Abstract: 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.

Abstract: 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.

Abstract: Methods and apparatus for spiking neural network computing based on e.g., a multi-layer kernel architecture, shared dendritic encoding, and/or thresholding of accumulated spiking signals. In one embodiment, a thresholding accumulator is disclosed that reduces spiking activity between different stages of a neuromorphic processor. Spiking activity can be directly related to power consumption and signal-to-noise ratio (SNR); thus, various embodiments trade-off the costs and benefits associated with threshold accumulation. For example, reducing spiking activity (e.g., by a factor of 10) during an encoding stage can have minimal impact on downstream fidelity (SNR) for a decoding stage, while yielding substantial improvements in power consumption.