In recent years, the explosive development of artificial intelligence implementing by artificial
neural networks (ANNs) creates inconceivable demands for computing hardware. However …

Sensing technology is an important aspect of information processing. Current development
in artificial intelligence systems (especially those aimed at medical and environmental …

Understanding how the structural organization of neural networks influences their
computational capabilities is of great interest to both machine learning and neuroscience …

Reservoir computing (RC) has attracted significant interest as a framework for the
implementation of novel neuromorphic computing architectures. Previously attention has …

Reservoir computation models form a subclass of recurrent neural networks with fixed non-
trainable input and dynamic coupling weights. Only the static readout from the state space …

This article is part of the Physical Review Research collection titled Physics of
Neuroscience. Correlated fluctuations in the activity of neural populations reflect the …

Optical Neural Networks (ONNs) can be promising alternatives for conventional electrical
neural networks as they offer ultra-fast data processing with low energy consumption …

Cascading activity is commonly observed in complex dynamical systems, including networks
of biological neurons, and how these cascades spread through the system is reliant on how …

For scientists in numerous fields, creating a physical device that can function like the human
brain is an aspiration. It is believed that we may achieve brain-like spatiotemporal …

We construct a deep learning-based numerical algorithm to solve path-dependent partial
differential equations arising in the context of rough volatility. Our approach is based on …