The perceptron learning algorithm and its multiple-layer extension, the backpropagation algorithm, are the foundations of the present-day machine learning revolution. However, these algorithms utilize a highly simplified mathematical abstraction of a neuron; it is not clear to what extent real biophysical neurons with morphologically-extended non-linear dendritic trees and conductance-based synapses can realize perceptron-like learning. Here we implemented the perceptron learning algorithm in a realistic biophysical model of a layer 5 cortical pyramidal cell with a full complement of non-linear dendritic channels. We tested this biophysical perceptron (BP) on a classification task, where it needed to correctly binarily classify 100, 1,000, or 2,000 patterns, and a generalization task, where it was required to discriminate between two “noisy” patterns. We show that the BP performs these tasks with an accuracy comparable to that of the original perceptron, though the classification capacity of the apical tuft is somewhat limited. We concluded that cortical pyramidal neurons can act as powerful classification devices.

中文翻译：

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建模皮质锥体细胞中的感知器学习和分类


感知器学习算法及其多层扩展（反向传播算法）是当今机器学习革命的基础。然而，这些算法利用了神经元的高度简化的数学抽象；目前尚不清楚具有形态学扩展的非线性树突树和基于电导的突触的真实生物物理神经元能够在多大程度上实现类似感知器的学习。在这里，我们在具有完整的非线性树突通道的第 5 层皮质锥体细胞的真实生物物理模型中实现了感知器学习算法。我们在分类任务和泛化任务上测试了这个生物物理感知器 (BP)，其中它需要正确地对 100、1,000 或 2,000 个模式进行二元分类，而在泛化任务中，它需要区分两个“嘈杂”模式。我们表明，尽管顶端簇的分类能力有些有限，但 BP 执行这些任务的精度与原始感知器相当。我们得出的结论是，皮质锥体神经元可以充当强大的分类装置。