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 …

Transcranial alternating current stimulation (tACS) seems likely to open a new era of the
field of noninvasive electrical stimulation of the human brain by directly interfering with …

Spiking neural networks (SNNs) promise to bridge the gap between artificial neural
networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible …

Distinct subtypes of inhibitory interneuron are known to shape diverse rhythmic activities in
the cortex, but how they interact to orchestrate specific band activity remains largely …

The training of multilayer spiking neural networks (SNNs) using the error backpropagation
algorithm has made significant progress in recent years. Among the various training …

Conduction time is typically ignored in computational models of neural network function.
Here we consider the effects of conduction delays on the synchrony of neuronal activity and …

The representation of external stimuli in the form of action potentials or spikes constitutes the
basis of energy efficient neural computation that emerging spiking neural networks (SNNs) …

Determining how neuronal activity represents sensory information is central for
understanding perception. Recent work shows that neural responses at different timescales …

Understanding how neural systems integrate, encode, and compute information is central to
understanding brain function. Frequently, data from neuroscience experiments are …

The brain is a highly modular structure. To exploit modularity, it is necessary that spiking
activity can propagate from one module to another while preserving the information it …