Piecewise linear neuron modeling
    4.
    发明授权
    Piecewise linear neuron modeling 有权
    分段线性神经元建模

    公开(公告)号:US09292790B2

    公开(公告)日:2016-03-22

    申请号:US14070652

    申请日:2013-11-04

    Abstract: Methods and apparatus for piecewise linear neuron modeling and implementing artificial neurons in an artificial nervous system based on linearized neuron models. One example method for operating an artificial neuron generally includes determining that a first state of the artificial neuron is within a first region; determining a second state of the artificial neuron based at least in part on a first set of linear equations, wherein the first set of linear equations is based at least in part on a first set of parameters corresponding to the first region; determining that the second state of the artificial neuron is within a second region; and determining a third state of the artificial neuron based at least in part on a second set of linear equations, wherein the second set of linear equations is based at least in part on a second set of parameters corresponding to the second region.

    Abstract translation: 基于线性神经元模型的人造神经系统中分段线性神经元建模和实现人造神经元的方法和装置。 用于操作人造神经元的一个示例性方法通常包括确定人造神经元的第一状态在第一区域内; 至少部分地基于第一组线性方程确定人造神经元的第二状态,其中所述第一组线性方程式至少部分地基于对应于所述第一区域的第一组参数; 确定人造神经元的第二状态在第二区域内; 以及至少部分地基于第二组线性方程确定所述人造神经元的第三状态,其中所述第二组线性方程式至少部分地基于对应于所述第二区域的第二组参数。

    Piecewise linear neuron modeling
    5.
    发明授权

    公开(公告)号:US09477926B2

    公开(公告)日:2016-10-25

    申请号:US14070679

    申请日:2013-11-04

    Abstract: Methods and apparatus for piecewise linear neuron modeling and implementing artificial neurons in an artificial nervous system based on linearized neuron models. One example method for operating an artificial neuron generally includes determining that a first state of the artificial neuron is within a first region; determining a second state of the artificial neuron based at least in part on a first set of linear equations, wherein the first set of linear equations is based at least in part on a first set of parameters corresponding to the first region; determining that the second state of the artificial neuron is within a second region; and determining a third state of the artificial neuron based at least in part on a second set of linear equations, wherein the second set of linear equations is based at least in part on a second set of parameters corresponding to the second region.

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