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

New device concepts and new computing principles are needed to balance our ever‐
growing appetite for data and information with the realization of the goals of increased …

The performance of complex networks, like the brain, depends on how effectively their
elements communicate. Despite the importance of communication, it is virtually unknown …

One of the essential functions of biological neural networks is the processing of information.
This includes everything from processing sensory information to perceive the environment …

Activity-dependent self-organization plays an important role in the formation of specific and
stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and …

Complex systems are characterized by multiple spatial and temporal scales. A natural
framework to capture their multiscale nature is that of multilayer networks, where different …

Functional-effective connectivity and network topology are nowadays key issues for studying
brain physiological functions and pathologies. Inferring neuronal connectivity from …

The analysis of neural systems leverages tools from many different fields. Drawing on
techniques from the study of critical phenomena in statistical mechanics, several studies …

Recent work has shown that functional connectivity among cortical neurons is highly varied,
with a small percentage of neurons having many more connections than others. Also, recent …

Transfer entropy (TE) is a widely used measure of directed information flows in a number of
domains including neuroscience. Many real-world time series for which we are interested in …