Abstract: A bipartite memristive network and method of teaching such a network is described herein. In one example case, the memristive network can include a number of nanofibers, wherein each nanofiber comprises a metallic core and a memristive shell. The memristive network can also include a number of electrodes deposited upon the nanofibers. A first set of the number of electrodes can include input electrodes in the memristive network, and a second set of the number of electrodes can include output electrodes in the memristive network. The memristive network can be embodied as a bipartite memristive network and trained according to the method of teaching described herein.

Abstract: Described herein are chemically assembled nanoparticles of a multiferroic material embedded into a conductive (e.g., metal-organic) framework host that allows for tunable qubit spacing and overall architecture. In certain aspects, the composites described herein can function as solid-state qubits. In other aspects, the composites described herein can be implemented in systems used in quantum information processing (QIP). In other aspects, the composites described herein can be used as a quantum sensor.

Abstract: Memristive learning concepts for neuromorphic circuits are described. In one example case, a neuromorphic circuit includes a first oscillatory-based neuron that generates a first oscillatory signal, a diode that rectifies the first oscillatory signal, and a synapse coupled to the diode and including a long-term potentiation (LTP) memristor arranged in parallel with a long-term depression (LTD) memristor. The circuit further includes a difference amplifier coupled to the synapse that generates a difference signal based on a difference between output signals from the LTP and LTD memristors, and a second oscillatory-based neuron electrically coupled to the difference amplifier that generates a second oscillatory signal based on the difference signal. The circuit also includes a feedback circuit that provides a feedback signal to the LTP and LTD memristors based on a difference or error between a target signal and the second oscillatory signal.

Abstract: Learning algorithms for oscillatory memristive neuromorphic circuits are described. In one embodiment, a neuromorphic circuit learning network includes a number of neuromorphic circuit nodes, each including a recognition neuron unit and a generative neuron unit. The learning network further includes a plurality of neuromorphic circuit feedforward couplings between the recognition neuron units in the neuromorphic circuit nodes, and a plurality of neuromorphic circuit feedback couplings between the generative neuron units in the neuromorphic circuit nodes. The learning network also includes a learning controller configured to drive activity among the recognition neuron units and train the generative neuron units for learning in one mode and to drive activity among the generative neuron units and train the recognition neuron units for learning in another mode. Various deep learning algorithms can be implemented in the learning network.

Abstract: Memristive learning concepts for neuromorphic circuits are described. In one example case, a neuromorphic circuit includes a first oscillatory-based neuron that generates a first oscillatory signal, a diode that rectifies the first oscillatory signal, and a synapse coupled to the diode and including a long-term potentiation (LTP) memristor arranged in parallel with a long-term depression (LTD) memristor. The circuit further includes a difference amplifier coupled to the synapse that generates a difference signal based on a difference between output signals from the LTP and LTD memristors, and a second oscillatory-based neuron electrically coupled to the difference amplifier that generates a second oscillatory signal based on the difference signal. The circuit also includes a feedback circuit that provides a feedback signal to the LTP and LTD memristors based on a difference or error between a target signal and the second oscillatory signal.

Abstract: A bipartite memristive network and method of teaching such a network is described herein. In one example case, the memristive network can include a number of nanofibers, wherein each nanofiber comprises a metallic core and a memristive shell. The memristive network can also include a number of electrodes deposited upon the nanofibers. A first set of the number of electrodes can include input electrodes in the memristive network, and a second set of the number of electrodes can include output electrodes in the memristive network. The memristive network can be embodied as a bipartite memristive network and trained according to the method of teaching described herein.

Abstract: The present invention relates to a valve cover assembly adapted to be secured to a cylinder head of a vehicle engine. The valve cover assembly includes a valve cover body formed from a first material and an attachment member formed from a second material and attached to the valve cover body.