公开(公告)号：US20190311263A1

公开(公告)日：2019-10-10

申请号：US16345533

申请日：2017-10-27

Applicant: University of Florida Research Foundation, Inc.

Inventor： Jack D. KENDALL ,  Juan C. NINO

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063 ,  G11C11/54

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.

公开(公告)号：US20190318242A1

公开(公告)日：2019-10-17

申请号：US16345551

申请日：2017-10-27

Applicant: University of Florida Research Foundation, Inc.

Inventor： Jack D. KENDALL ,  Juan C. NINO

IPC: G06N3/08 ,  G06N3/063

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. Two examples include the wake-sleep algorithm for unsupervised neural networks and target propagation.