Fault-Tolerant Neural Networks from Biological Error Correction Codes

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 …

analog cells computation cortex cs.lg cs.ne deep learning error error correction grid information networks neural networks neurons noise processing protect q-bio.nc question role stat.ml