The potential for neuromorphic computing to provide intrinsic fault tolerance has long been speculated, but the brain's robustness in neuromorphic applications has yet to be demonstrated. Here, we show that a previously described, natively spiking neuromorphic algorithm for solving partial differential equations is intrinsically tolerant to structural perturbations in the form of ablated neurons and dropped spikes. The tolerance band for these perturbations is large: we find that as many as 32 percent of the neurons and up to 90 percent of the spikes may be entirely dropped before a significant degradation in the accuracy results. Furthermore, this robustness is tunable through structural hyperparameters. This work demonstrates that the specific brain-like inspiration behind the algorithm contributes to a significant degree of robustness expected from brain-like neuromorphic algorithms.

翻译：神经形态计算具备固有容错能力的潜力长期以来备受推测，但大脑在神经形态应用中的鲁棒性尚未得到证实。本文表明，一种先前描述的、用于求解偏微分方程的本征脉冲神经形态算法，对神经元消融和脉冲丢失形式的结构扰动具有固有耐受性。这些扰动的耐受范围很大：我们发现，在多达32%的神经元和高达90%的脉冲完全丢失后，计算精度才出现显著下降。此外，这种鲁棒性可通过结构超参数进行调节。本工作证明，该算法背后特定的类脑灵感为其带来了类脑神经形态算法所预期的高度鲁棒性。