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

Inspiration for artificial biologically inspired computing is often drawn from neural systems.
This article shows how to analyze neural systems using information theory with the aim of …

While single-cell gene expression experiments present new challenges for data processing,
the cell-to-cell variability observed also reveals statistical relationships that can be used by …

We begin by reviewing the statistical framework of information theory as applicable to
neuroimaging data analysis. A major factor hindering wider adoption of this framework in …

The global biodiversity crisis is one of humanity's most urgent problems, but even
quantifying biological diversity is a difficult mathematical and conceptual challenge. This …

Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages
in vivo. While much is known about the molecular basis of pluripotency, the mechanisms of …

The broad concept of emergence is instrumental in various of the most challenging open
scientific questions—yet, few quantitative theories of what constitutes emergent phenomena …

How does the organization of neural information processing enable humans' sophisticated
cognition? Here we decompose functional interactions between brain regions into …

Neural coding has traditionally been examined through changes in firing rates and latencies
in response to different stimuli,,,–. However, populations of neurons can also exhibit …

The formulation of the Partial Information Decomposition (PID) framework by Williams and
Beer in 2010 attracted a significant amount of attention to the problem of defining redundant …