Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

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 neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …

Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …

The brain functions as a spatio-temporal information processing machine. Spatio-and
spectro-temporal brain data (STBD) are the most commonly collected data for measuring …

On-line learning and recognition of spatio-and spectro-temporal data (SSTD) is a very
challenging task and an important one for the future development of autonomous machine …

The concept that neural information is encoded in the firing rate of neurons has been the
dominant paradigm in neurobiology for many years. This paradigm has also been adopted …

Emotion recognition still poses a challenge lying at the core of the rapidly growing area of
affective computing and is crucial for establishing a successful human–computer interaction …

Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design
efficient SNN systems, real-valued signals must be optimally encoded into spike trains so …