Skip to main content
Video s3
    Details
    Presenter(s)
    Jason Eshraghian Headshot
    Display Name
    Jason Eshraghian
    Affiliation
    Affiliation
    University of Michigan
    Country
    Author(s)
    Display Name
    Steve Kang
    Affiliation
    Affiliation
    University of California, Santa Cruz
    Display Name
    Donguk Choi
    Affiliation
    Affiliation
    University of California, Santa Cruz
    Display Name
    Jason Eshraghian
    Affiliation
    Affiliation
    University of Michigan
    Display Name
    Peng Zhou
    Affiliation
    Affiliation
    University of California, Santa Cruz
    Display Name
    Jieun Kim
    Affiliation
    Affiliation
    Sungkyunkwan University
    Display Name
    Bai-Sun Kong
    Affiliation
    Affiliation
    Sungkyunkwan University
    Display Name
    Xiaojian Zhu
    Affiliation
    Affiliation
    Institute of Materials Technology, Chinese Academy of Sciences
    Affiliation
    Affiliation
    University of Dresden
    Display Name
    Alon Ascoli
    Affiliation
    Affiliation
    Technical University Dresden
    Display Name
    Ronald Tetzlaff
    Affiliation
    Affiliation
    Technische Universität Dresden
    Display Name
    Wei D. Lu
    Affiliation
    Affiliation
    University of Michigan, Ann Arbor
    Display Name
    Leon Chua
    Affiliation
    Affiliation
    University of California, Berkeley
    Abstract

    We present and experimentally validate two minimal compact memristive models for spiking neuronal signal generation using commercially available low-cost components. The Memristive Integrate-and-Fire (MIF) model for neuronal signaling with two voltage levels: the spike-peak, and the rest-potential. The second model MIF2 promotes local adaptation by accounting for a third refractory voltage level during hyperpolarization. Analytical projections show that a memristive solid-state brain could be realized within (i) the surface area of the median human brain, 2,400cm2, (ii) the same volume of the median human brain, and (iii) a total power budget of approximately 20 W using a 3.5 nm technology.

    Slides
    • How to Build a Memristive Integrate-and-Fire Model for Spiking Neuronal Signal Generation (application/pdf)