title: Qualitative-Modeling-Based Silicon Neurons and Their Networks.
abstract: The ionic conductance models of neuronal cells can finely reproduce a wide variety of complex neuronal activities. However, the complexity of these models has prompted the development of qualitative neuron models. They are described by differential equations with a reduced number of variables and their low-dimensional polynomials, which retain the core mathematical structures. Such simple models form the foundation of a bottom-up approach in computational and theoretical neuroscience. We proposed a qualitative-modeling-based approach for designing silicon neuron circuits, in which the mathematical structures in the polynomial-based qualitative models are reproduced by differential equations with silicon-native expressions. This approach can realize low-power-consuming circuits that can be configured to realize various classes of neuronal cells. In this article, our qualitative-modeling-based silicon neuron circuits for analog and digital implementations are quickly reviewed. One of our CMOS analog silicon neuron circuits can realize a variety of neuronal activities with a power consumption less than 72 nW. The square-wave bursting mode of this circuit is explained. Another circuit can realize Class I and II neuronal activities with about 3 nW. Our digital silicon neuron circuit can also realize these classes. An auto-associative memory realized on an all-to-all connected network of these silicon neurons is also reviewed, in which the neuron class plays important roles in its performance.
authors.affiliation: Institute of Industrial Science, University of Tokyo Tokyo, Japan.
authors.forename: Takashi
authors.initials: T
authors.lastname: Kohno
authors.affiliation: Department of Mechanical and Intelligent Engineering, Utsunomiya University Utsunomiya, Japan.
authors.forename: Munehisa
authors.initials: M
authors.lastname: Sekikawa
authors.affiliation: College of Electronic Engineering, Xi'an Shiyou University Xi'an, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Electrical Engineering and Information Systems, University of Tokyo Tokyo, Japan.
authors.forename: Takuya
authors.initials: T
authors.lastname: Nanami
authors.affiliation: Institute of Industrial Science, University of Tokyo Tokyo, Japan.
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
keywords: low-power circuit; neuronal network emulation; non-linear dynamics; qualitative modeling; silicon neuron