In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in
spiking neural networks (SNN) to extract visual features of low or intermediate complexity in …

A central problem in cognitive science and behavioural neuroscience as well as in machine
learning and artificial intelligence research is to ascertain whether two or more decision …

The goal of computational neuroscience is to find mechanistic explanations of how the
nervous system processes information to support cognitive function and behaviour. At the …

Reinforcement learning (RL) has recently regained popularity with major achievements such
as beating the European game of Go champion. Here, for the first time, we show that RL can …

Over the last several years, the use of machine learning (ML) in neuroscience has been
rapidly increasing. Here, we review ML's contributions, both realized and potential, across …

Core object recognition, the ability to rapidly recognize objects despite variations in their
appearance, is largely solved through the feedforward processing of visual information …

For decades, perception was considered a unique ability of biological systems, little
understood in its inner workings, and virtually impossible to match in artificial systems. But …

Recent studies showed agreement between how the human brain and neural networks
represent objects, suggesting that we might start to understand the underlying computations …