论文标题
单个神经元可以解决mnist吗？生物树突树的计算能力
Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees
论文作者
论文摘要
生理实验强调了生物神经元的树突如何非线性过程分布的突触输入。这与人工神经网络中的单元形成了鲜明的对比，这些单元通常是线性与输出非线性不同的。如果树突状树可能是非线性的，那么生物神经元可能比人工副标具有更多的计算能力。在这里，我们使用一个简单的模型，其中将树突作为一系列阈值线性单元实现。我们发现，这种树突可以很容易地解决机器学习问题，例如MNIST或CIFAR-10，并且由于在树突树的多个分支上具有相同的输入而受益。该树突模型是稀疏网络的特殊情况。这项工作表明，流行的神经元模型可能严重低估了通过非线性树突的生物学事实和每对神经元多个突触所启用的计算能力。下一代人工神经网络可能会从这些生物学启发的树突结构中受益匪浅。
Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. This is in stark contrast to units in artificial neural networks that are generally linear apart from an output nonlinearity. If dendritic trees can be nonlinear, biological neurons may have far more computational power than their artificial counterparts. Here we use a simple model where the dendrite is implemented as a sequence of thresholded linear units. We find that such dendrites can readily solve machine learning problems, such as MNIST or CIFAR-10, and that they benefit from having the same input onto several branches of the dendritic tree. This dendrite model is a special case of sparse network. This work suggests that popular neuron models may severely underestimate the computational power enabled by the biological fact of nonlinear dendrites and multiple synapses per pair of neurons. The next generation of artificial neural networks may significantly benefit from these biologically inspired dendritic architectures.
