Rowcliffe Phill, Feng Jianfeng, Buxton Hilary
IEEE Trans Neural Netw. 2006 May;17(3):803-7. doi: 10.1109/TNN.2006.873274.
A more plausible biological version of the traditional perceptron is presented here with a learning rule which enables training of the neuron on nonlinear tasks. Three different models are introduced with varying inhibitory and excitatory synaptic connections. Using the derived learning rule, a single neuron is trained to successfully classify the XOR problem.
本文提出了一种更合理的传统感知器生物学版本，带有一种学习规则，该规则能使神经元在非线性任务上进行训练。引入了三种具有不同抑制性和兴奋性突触连接的不同模型。利用推导得出的学习规则，训练单个神经元成功对异或问题进行分类。
