Editorial: Artificial Neural Networks as Models of Neural Information Processing

Editorial on the Research Topic Artificial Neural Networks as Models of Neural Information Processing Introduction In artificial intelligence (AI), new advances make it possible that artificial neural networks (ANNs) learn to solve complex problems in a reasonable amount of time (LeCun et al., 2015). Folli et al. show that by allowing non-zero diagonal elements on the weight matrix, maximal storage capacity is obtained when the number of stored memory patterns exceeds the network size. The rate with which spikes are emitted is often mapped to the analog activation values of artificial neurons, but it is well-known that this relationship captures only part of the information processing in real neurons.