Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
PLoS Comput Biol. 2023 Apr 4;19(4):e1011005. doi: 10.1371/journal.pcbi.1011005. eCollection 2023 Apr.
When a neuron is driven beyond its threshold, it spikes. The fact that it does not communicate its continuous membrane potential is usually seen as a computational liability. Here we show that this spiking mechanism allows neurons to produce an unbiased estimate of their causal influence, and a way of approximating gradient descent-based learning. Importantly, neither activity of upstream neurons, which act as confounders, nor downstream non-linearities bias the results. We show how spiking enables neurons to solve causal estimation problems and that local plasticity can approximate gradient descent using spike discontinuity learning.
当神经元被驱动超过其阈值时，它就会产生尖峰。事实上，神经元不传递其连续膜电位通常被视为一种计算上的不利因素。在这里，我们表明这种尖峰机制允许神经元对其因果影响进行无偏估计，并提供了一种基于梯度下降的学习方法的近似方法。重要的是，上游神经元的活动（作为混杂因素）和下游非线性都不会对结果产生偏差。我们展示了尖峰如何使神经元能够解决因果估计问题，以及局部可塑性如何使用尖峰不连续学习来近似梯度下降。
