It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error correction codes have been observed to protect states against neural spiking noise, but their role in information processing is unclear. Here, we use these biological error correction codes to develop a universal fault-tolerant neural network that achieves reliable computation if the faultiness of each neuron lies below a sharp threshold; remarkably, we find that noisy biological neurons fall below this threshold. The discovery of a phase transition from faulty to fault-tolerant neural computation suggests a mechanism for reliable computation in the cortex and opens a path towards understanding noisy analog systems relevant to artificial intelligence and neuromorphic computing.

翻译：在深度学习中，容错计算是否可实现一直是一个悬而未决的问题：能否仅使用不可靠的神经元实现任意可靠的计算？在哺乳动物大脑皮层的网格细胞中，已观察到模拟纠错码能够保护状态免受神经脉冲噪声的影响，但其在信息处理中的作用尚不明确。在此，我们利用这些生物纠错码开发了一种通用容错神经网络，当每个神经元的故障率低于某个尖锐阈值时，该网络可实现可靠计算；值得注意的是，我们发现嘈杂的生物神经元恰好低于这一阈值。从有故障到容错神经计算的相变发现，揭示了皮层中可靠计算的机制，并为理解与人工智能和神经形态计算相关的嘈杂模拟系统开辟了一条新路径。