神经科学
计算机科学
阈值
非线性系统
人工神经网络
突触可塑性
树枝状尖峰
噪音(视频)
神经元
人工智能
生物
兴奋性突触后电位
物理
抑制性突触后电位
生物化学
受体
量子力学
图像(数学)
摘要
The vast computational power of the brain has traditionally been viewed as arising from the complex connectivity of neural networks, in which an individual neuron acts as a simple linear summation and thresholding device. However, recent studies show that individual neurons utilize a wealth of nonlinear mechanisms to transform synaptic input into output firing. These mechanisms can arise from synaptic plasticity, synaptic noise, and somatic and dendritic conductances. This tool kit of nonlinear mechanisms confers considerable computational power on both morphologically simple and more complex neurons, enabling them to perform a range of arithmetic operations on signals encoded ina variety of different ways.
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