IEMN, CNRS UMR 8520, Villeneuve d'asq, 59650, France.
Institut de la vision, CNRS, INSERM, Paris, 75012, France.
F1000Res. 2020 Sep 28;9:1174. doi: 10.12688/f1000research.26486.3. eCollection 2020.
In theory, neurons modelled as single layer perceptrons can implement all linearly separable computations. In practice, however, these computations may require arbitrarily precise synaptic weights. This is a strong constraint since both biological neurons and their artificial counterparts have to cope with limited precision. Here, we explore how non-linear processing in dendrites helps overcome this constraint. We start by finding a class of computations which requires increasing precision with the number of inputs in a perceptron and show that it can be implemented without this constraint in a neuron with sub-linear dendritic subunits. Then, we complement this analytical study by a simulation of a biophysical neuron model with two passive dendrites and a soma, and show that it can implement this computation. This work demonstrates a new role of dendrites in neural computation: by distributing the computation across independent subunits, the same computation can be performed more efficiently with less precise tuning of the synaptic weights. This work not only offers new insight into the importance of dendrites for biological neurons, but also paves the way for new, more efficient architectures of artificial neuromorphic chips.
从理论上讲，模拟为单层感知器的神经元可以实现所有可线性分离的计算。然而，在实践中，这些计算可能需要任意精确的突触权重。这是一个很强的约束，因为生物神经元及其人工对应物都必须应对有限的精度。在这里，我们探讨了树突中的非线性处理如何帮助克服这一限制。我们首先找到一类计算，这些计算需要随着感知器输入数量的增加而提高精度，并表明在具有次线性树突亚基的神经元中，可以在没有这种约束的情况下实现这些计算。然后，我们通过模拟具有两个被动树突和一个胞体的生物物理神经元模型来补充这个分析研究，并表明它可以实现这个计算。这项工作展示了树突在神经计算中的新作用：通过将计算分布在独立的亚单位中，相同的计算可以通过更精确地调整突触权重来更有效地进行。这项工作不仅为生物神经元中树突的重要性提供了新的见解，而且为人工神经形态芯片的新的、更有效的架构铺平了道路。
