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In neuroscience and computer science, synaptic weight refers to the strength or amplitude of a connection between two nodes, corresponding in biology to the amount of influence the firing of one neuron has on another. The term is typically used in artificial and biological neural network research. ==Computation== In a computational neural network, a vector or set of inputs and outputs , or pre- and post-synaptic neurons respectively, are interconnected with synaptic weights represented by the matrix , where for a linear neuron :. The synaptic weight is changed by using a learning rule, the most basic of which is Hebb's rule, which is usually stated in biological terms as ''Neurons that fire together, wire together.'' Computationally, this means that if a large signal from one of the input neurons results in a large signal from one of the output neurons, then the synaptic weight between those two neurons will increase. The rule is unstable, however, and is typically modified using such variations as Oja's rule, radial basis functions or the backpropagation algorithm. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Synaptic weight」の詳細全文を読む スポンサード リンク
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