A central goal of neuroscience is to understand the representations formed by brain activity
patterns and their connection to behaviour. The classic approach is to investigate how …

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

Of the various attempts to generalize information theory to multiple variables, the most
widely utilized, interaction information, suffers from the problem that it is sometimes negative …

Neurons in mouse primary visual cortex (V1) are selective for particular properties of visual
stimuli. Locomotion causes a change in cortical state that leaves their selectivity unchanged …

The problem of how to properly quantify redundant information is an open question that has
been the subject of much recent research. Redundant information refers to information about …

Tuning curves are widely used to characterize the responses of sensory neurons to external
stimuli, but there is an ongoing debate as to their role in sensory processing. Commonly, it is …

By investigating the topology of neuronal co-activity, we found that mnemonic information
spans multiple operational axes in the mouse hippocampus network. High-activity principal …

Excitation is balanced by inhibition to cortical neurons across a wide range of conditions. To
understand how this relationship is maintained, we broadly suppressed the activity of …

There has been considerable debate in the literature about the relative merits of information
processing versus dynamical approaches to understanding cognitive processes. In this …

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 …