title: Complex Learning in Bio-plausible Memristive Networks.
abstract: The emerging memristor-based neuromorphic engineering promises an efficient computing paradigm. However, the lack of both internal dynamics in the previous feedforward memristive networks and efficient learning algorithms in recurrent networks, fundamentally limits the learning ability of existing systems. In this work, we propose a framework to support complex learning functions by introducing dedicated learning algorithms to a bio-plausible recurrent memristive network with internal dynamics. We fabricate iron oxide memristor-based synapses, with well controllable plasticity and a wide dynamic range of excitatory/inhibitory connection weights, to build the network. To adaptively modify the synaptic weights, the comprehensive recursive least-squares (RLS) learning algorithm is introduced. Based on the proposed framework, the learning of various timing patterns and a complex spatiotemporal pattern of human motor is demonstrated. This work paves a new way to explore the brain-inspired complex learning in neuromorphic systems.
authors.affiliation: 1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Deng
authors.affiliation: 1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
authors.forename: Guoqi
authors.initials: G
authors.lastname: Li
authors.affiliation: 1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Institute of Microelectronics, Tsinghua University, Beijing 100084, China.
authors.forename: Ning
authors.initials: N
authors.lastname: Deng
authors.affiliation: 1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wang
authors.affiliation: 1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
authors.forename: Ziyang
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Engineering Product Design, Singapore University of Technology and Design, Singapore 138682.
authors.forename: Wei
authors.initials: W
authors.lastname: He
authors.affiliation: 1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
authors.forename: Huanglong
authors.initials: H
authors.lastname: Li
authors.affiliation: 1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Pei
authors.affiliation: 1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
authors.forename: Luping
authors.initials: L
authors.lastname: Shi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D006801:Humans; D000069550:Machine Learning; D008959:Models, Neurological; D008962:Models, Theoretical; D016571:Neural Networks, Computer