Neuronal arithmetic.
Neuronal arithmetic.
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DOI：
10.1038/nrn2864
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发表时间：
2010-07
期刊：
影响因子：
--
通讯作者：
Silver RA
中科院分区：
文献类型：
--
作者：
Silver RA
传统上，大脑的巨大计算能力被认为是由神经网络的复杂连接引起的，其中单个神经元充当简单的线性求和和阈值设备。然而，最近的研究表明，单个神经元利用丰富的非线性机制将突触输入转换为输出放电。这些机制可能源于突触可塑性、突触噪声以及体细胞和树突电导。这种非线性机制的工具包赋予了形态简单和更复杂的神经元相当大的计算能力，使它们能够对以各种不同方式编码的信号执行一系列算术运算。
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 in a variety of different ways.