Addressing the multiscale organization of the brain, which is fundamental to the dynamic
repertoire of the organ, remains challenging. In principle, it should be possible to model …

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 …

“Neural coding” is a popular metaphor in neuroscience, where objective properties of the
world are communicated to the brain in the form of spikes. Here I argue that this metaphor is …

A fundamental problem in neuroscience is understanding how sequences of action
potentials (“spikes”) encode information about sensory signals and motor outputs. Although …

Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …

Millisecond variations in spiking patterns can radically alter motor behavior, suggesting that
traditional rate-based theories of motor control require revision. The importance of spike …

Brain oscillations involve rhythmic fluctuations of neuronal excitability and may play a crucial
role in neural communication. The human corticomuscular system is characterized by beta …

The following preface is the one that we published in Volume 1 of the Springer Handbook of
Auditory Research back in 1992. As anyone reading the original preface, or the many users …

The rate and temporal pattern of neural spiking each have the potential to influence
computation. In the cerebellum, it has been hypothesized that the irregularity of interspike …

Estimation of mutual information between (multidimensional) real-valued variables is used
in analysis of complex systems, biological systems, and recently also quantum systems. This …