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biological neuron model
A biological neuron model (also known as spiking neuron model) is a mathematical description of the properties of nerve cells, or neurons, that is designed to accurately describe and predict biological processes. This is in contrast to the artificial neuron, which aims for computational effectiveness, although these goals sometimes overlap.
==Artificial neuron abstraction==
The most basic model of a neuron consists of an input with some synaptic weight vector and an activation function or transfer function inside the neuron determining output. This is the basic structure used in artificial neurons, which in a neural network often looks like
where is the output of the th neuron, is the th input neuron signal, is the synaptic weight (or strength of connection) between the neurons and , and is the activation function. While this model has seen success in machine-learning applications, it is a poor model for real (biological) neurons, because it lacks the time-dependence that real neurons exhibit. Some of the earliest biological models took this form until kinetic models such as the Hodgkin–Huxley model became dominant.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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