Mathematical and statistical models have played important roles in neuroscience, especially
by describing the electrical activity of neurons recorded individually, or collectively across …

Though the temporal precision of neural computation has been studied intensively, a data-
driven determination of this precision remains a fundamental challenge. Reproducible spike …

In recent years, the field of neuroscience has gone through rapid experimental advances
and a significant increase in the use of quantitative and computational methods. This growth …

Evolution has conserved “economic” systems that perform many functions, faster or better,
with less. For example, three to five leukocyte types protect from thousands of pathogens. To …

Abstract Machine learning optimizes flexible models to predict data. In scientific applications,
there is a rising interest in interpreting these flexible models to derive hypotheses from data …

The responses of sensory neurons can be quite different to repeated presentations of the
same stimulus. Here, we demonstrate a direct link between the trial-to-trial variability of …

Many complex systems operating far from the equilibrium exhibit stochastic dynamics that
can be described by a Langevin equation. Inferring Langevin equations from data can …

With the ability to observe the activity from large numbers of neurons simultaneously using
modern recording technologies, the chance to identify sub-networks involved in coordinated …

Whether, when, and how causal interactions between neurons can be meaningfully studied
from observations of neural activity alone are vital questions in neural data analysis. Here …

Electroencephalography (EEG) provides a non-invasive measure of brain electrical activity.
Neural population models, where large numbers of interacting neurons are considered …