The design of robots that interact autonomously with the environment and exhibit complex
behaviours is an open challenge that can benefit from understanding what makes living …

Synapses are essential to the transmission of nervous signals. Synaptic plasticity allows
changes in synaptic strength that make a brain capable of learning from experience. During …

In order to understand how the mammalian neocortex is performing computations, two
things are necessary; we need to have a good understanding of the available neuronal …

A striking difference between brain-inspired neuromorphic processors and current von
Neumann processor architectures is the way in which memory and processing is organized …

Shifting computing architectures from von Neumann to event-based spiking neural networks
(SNNs) uncovers new opportunities for low-power processing of sensory data in …

Working memory is a fundamental feature of biological brains for perception, cognition, and
learning. In addition, learning with working memory, which has been show in conventional …

In this Review, memristors are examined from the frameworks of both von Neumann and
neuromorphic computing architectures. For the former, a new logic computational process …

This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …

In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

The ever‐increasing processing power demands of digital computers cannot continue to be
fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing …