An accepted conclusion for modeling a physiological neuron is its divisibility into two mathematical processes. These are the digital process and the analog process. In a 1943 paper McCulloch and Pitts presented a neural model performing switching logic (an IT digital process). Their model represents the basis of the model used in the well-known Perceptron, devised by Rosenblatt in 1957. The Perceptron is a neural network of models that included varying weights, which corresponds to a neuron’s analog process. This talk will define the digital and the analog processes. These processes will be associated with a physiological neuron’s anatomy.

The Perceptron’s neuron model continues to be used in current neural networks despite a serious limitation of only realizing linearly separable switching functions. Linearly separable switching functions will also be defined. The currently used “linear” neuron model will be shown to have severely limited switching logic realizations. Linear separability will also be defined.

A general non-linear neuron model will be defined, which is capable of performing all possible switching logic realizations. Examples of both a linear neuron model’s and nonlinear neuron models’ switching logic realizations will be included.

The proposed nonlinear neuron model, which more accurately takes into account actual neurophysiologic mechanisms, can form the basis of modeling network processes at all levels of the nervous system, including the brain.

对生理神经元建模的一个公认结论是它可分为两个数学过程。这些是数字过程和模拟过程。在 1943 年的一篇论文中，McCulloch 和 Pitts 提出了一个执行开关逻辑（IT 数字过程）的神经模型。他们的模型代表了著名的感知器中使用的模型的基础，该模型由 Rosenblatt 于 1957 年设计。感知器是一个包含不同权重的模型神经网络，它对应于神经元的模拟过程。本次演讲将定义数字和模拟过程。这些过程将与生理神经元的解剖结构相关联。

感知器的神经元模型继续在当前的神经网络中使用，尽管存在仅实现线性可分切换功能的严重限制。还将定义线性可分切换函数。当前使用的“线性”神经元模型将被证明具有严重受限的切换逻辑实现。还将定义线性可分性。

将定义通用非线性神经元模型，该模型能够执行所有可能的切换逻辑实现。将包括线性神经元模型和非线性神经元模型的切换逻辑实现的示例。

所提出的非线性神经元模型更准确地考虑了实际的神经生理机制，可以构成对神经系统各个层次（包括大脑）的网络过程进行建模的基础。