Comment on All-optical machine learning using diffractive deep neural networks

Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But interpreting the multilayer diffractive setup as a deep neural network and advocating it as an all-optical deep learning framework are not well justified and represent a mischaracterization of the system by overlooking its defining characteristics of perfect linearity and strict passivity.

Learning Temporal Structures of Random Patterns

A cornerstone of human statistical learning is the ability to extract temporal regularities / patterns from random sequences. Here we present a method of computing pattern time statistics with generating functions for first-order Markov trials and independent Bernoulli trials. We show that the pattern time statistics cover a wide range of measurements commonly used in existing studies of both human and machine learning of stochastic processes, including probability of alternation, temporal correlation between pattern events, and related variance / risk measures. Moreover, we show that recurrent processing and event segmentation by pattern overlap may provide a coherent explanation for the sensitivity of the human brain to the rich statistics and the latent structures in the learning environment.