Abstract: A method for setting memory elements in a plurality of states includes applying a set signal to a memory element to transition the memory element from a low-current state to a high-current state; applying a partial reset signal to the memory element to transition the memory element from the high-current state to a state between the high-current state and the low-current state; determining whether the state corresponds to a predetermined state; and applying one or more additional partial reset signals to the memory element until the state corresponds to the predetermined current state. The memory element may be coupled in series with a transistor, and a voltage control circuit may apply voltages to the transistor to set and partially reset the memory element.

Abstract: A method of setting multi-state memory elements into at least one low-power state may include receiving a command to cause a memory element to transition into one of three or more states; applying a first signal to the memory element to transition the memory element into the one of the three or more states, where the three or more states are evenly spaced in a portion of an operating range of the memory element; receiving a command to cause a memory element to transition into a low-power state; applying a second signal to the memory element to transition the memory element into the low-power state, where the low-power state is outside of the portion of the operating range of the memory element by an amount greater than a space between each of the three or more states.

Abstract: A method for setting memory elements in a plurality of states includes applying a set signal to a memory element to transition the memory element from a low-current state to a high-current state; applying a partial reset signal to the memory element to transition the memory element from the high-current state to a state between the high-current state and the low-current state; determining whether the state corresponds to a predetermined state; and applying one or more additional partial reset signals to the memory element until the state corresponds to the predetermined current state. The memory element may be coupled in series with a transistor, and a voltage control circuit may apply voltages to the transistor to set and partially reset the memory element.

Abstract: A method for setting memory elements in a plurality of states includes applying a set signal to a memory element to transition the memory element from a low-current state to a high-current state; applying a partial reset signal to the memory element to transition the memory element from the high-current state to a state between the high-current state and the low-current state; determining whether the state corresponds to a predetermined state; and applying one or more additional partial reset signals to the memory element until the state corresponds to the predetermined current state. The memory element may be coupled in series with a transistor, and a voltage control circuit may apply voltages to the transistor to set and partially reset the memory element.

Abstract: Sparse representation of information performs powerful feature extraction on high-dimensional data and is of interest for applications in signal processing, machine vision, object recognition, and neurobiology. Sparse coding is a mechanism by which biological neural systems can efficiently process complex sensory data while consuming very little power. Sparse coding algorithms in a bio-inspired approach can be implemented in a crossbar array of memristors (resistive memory devices). This network enables efficient implementation of pattern matching and lateral neuron inhibition, allowing input data to be sparsely encoded using neuron activities and stored dictionary elements. The reconstructed input can be obtained by performing a backward pass through the same crossbar matrix using the neuron activity vector as input. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals.

Abstract: Sparse representation of information performs powerful feature extraction on high-dimensional data and is of interest for applications in signal processing, machine vision, object recognition, and neurobiology. Sparse coding is a mechanism by which biological neural systems can efficiently process complex sensory data while consuming very little power. Sparse coding algorithms in a bio-inspired approach can be implemented in a crossbar array of memristors (resistive memory devices). This network enables efficient implementation of pattern matching and lateral neuron inhibition, allowing input data to be sparsely encoded using neuron activities and stored dictionary elements. The reconstructed input can be obtained by performing a backward pass through the same crossbar matrix using the neuron activity vector as input. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals.

Abstract: Sparse representation of information performs powerful feature extraction on high-dimensional data and is of interest for applications in signal processing, machine vision, object recognition, and neurobiology. Sparse coding is a mechanism by which biological neural systems can efficiently process complex sensory data while consuming very little power. Sparse coding algorithms in a bio-inspired approach can be implemented in a crossbar array of memristors (resistive memory devices). This network enables efficient implementation of pattern matching and lateral neuron inhibition, allowing input data to be sparsely encoded using neuron activities and stored dictionary elements. The reconstructed input can be obtained by performing a backward pass through the same crossbar matrix using the neuron activity vector as input. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals.

Abstract: Sparse representation of information performs powerful feature extraction on high-dimensional data and is of interest for applications in signal processing, machine vision, object recognition, and neurobiology. Sparse coding is a mechanism by which biological neural systems can efficiently process complex sensory data while consuming very little power. Sparse coding algorithms in a bio-inspired approach can be implemented in a crossbar array of memristors (resistive memory devices). This network enables efficient implementation of pattern matching and lateral neuron inhibition, allowing input data to be sparsely encoded using neuron activities and stored dictionary elements. The reconstructed input can be obtained by performing a backward pass through the same crossbar matrix using the neuron activity vector as input. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals.

Abstract: Sparse representation of information performs powerful feature extraction on high-dimensional data and is of interest for applications in signal processing, machine vision, object recognition, and neurobiology. Sparse coding is a mechanism by which biological neural systems can efficiently process complex sensory data while consuming very little power. Sparse coding algorithms in a bio-inspired approach can be implemented in a crossbar array of memristors (resistive memory devices). This network enables efficient implementation of pattern matching and lateral neuron inhibition, allowing input data to be sparsely encoded using neuron activities and stored dictionary elements. The reconstructed input can be obtained by performing a backward pass through the same crossbar matrix using the neuron activity vector as input. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals.

Abstract: Sparse representation of information performs powerful feature extraction on high-dimensional data and is of interest for applications in signal processing, machine vision, object recognition, and neurobiology. Sparse coding is a mechanism by which biological neural systems can efficiently process complex sensory data while consuming very little power. Sparse coding algorithms in a bio-inspired approach can be implemented in a crossbar array of memristors (resistive memory devices). This network enables efficient implementation of pattern matching and lateral neuron inhibition, allowing input data to be sparsely encoded using neuron activities and stored dictionary elements. The reconstructed input can be obtained by performing a backward pass through the same crossbar matrix using the neuron activity vector as input. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals.