Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

Time-series analysis including forecasting is essential in a range of fields from finance to
engineering. However, long-term forecasting is difficult, particularly for cases where the …

The connection patterns of neural circuits in the brain form a complex network. Collective
signalling within the network manifests as patterned neural activity and is thought to support …

The connection patterns of neural circuits form a complex network. How signaling in these
circuits manifests as complex cognition and adaptive behaviour remains the central question …

We present simulations based on a model of self-assembled nanowire networks with
memristive junctions and neural network-like topology. We analyze the dynamical voltage …

A concept of biomorphic neuroprocessor that implements hardware spiking neural network
for traditional tasks of information processing and can simulate operation of brain cortical …

Low-power and low-area neurons are essential for hardware implementation of large-scale
SNNs. Various novel-physics-based leaky-integrate-and-fire (LIF) neuron architectures have …

An electrical circuit, topology, and a fabrication technology have been developed for an ultra
large multilayer memory matrix having a non-volatile memory, low energy consumption and …

We propose reservoir computing (RC), a framework for constructing recurrent neural
networks (RNNs) with simple training rule, using ferroelectric tunnel junction (FTJ) crossbar …

Reservoir computing (RC) has received recent interest because reservoir weights do not
need to be trained, enabling extremely low-resource consumption implementations, which …