Biological and artificial information processing systems form representations that they can
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …

One major challenge of neuroscience is identifying structure in seemingly disorganized
neural activity. Different types of structure have different computational implications that can …

How can we tell whether two neural networks utilize the same internal processes for a
particular computation? This question is pertinent for multiple subfields of neuroscience and …

Measuring similarity of neural networks to understand and improve their behavior has
become an issue of great importance and research interest. In this survey, we provide a …

The signaling capacity of a neural population depends on the scale and orientation of its
covariance across trials. Estimating this" noise" covariance is challenging and is thought to …

Data-driven approaches demonstrate significant potential in accurately diagnosing faults in
wind turbines. To enhance diagnostic performance and reduce communication costs in …

The relationship between perception and inference, as postulated by Helmholtz in the 19th
century, is paralleled in modern machine learning by generative models like Variational …

Image representations (artificial or biological) are often compared in terms of their global
geometry; however, representations with similar global structure can have strikingly different …

A multitude of (dis) similarity measures between neural networks representations have been
proposed, resulting in a fragmented research landscape. Most (dis) similarity measures fall …

The Platonic Representation Hypothesis claims that recent foundation models are
converging to a shared representation space as a function of their downstream task …