State space methods have proven indispensable in neural data analysis. However, common
methods for performing inference in state-space models with non-Gaussian observations …

Highlights•Modern recording technologies are creating data at a scale and complexity that
demand rigorous data analytical approaches.•Neural data science is an essential bridge …

Highlights•Computational models will prove increasingly useful for understanding large
datasets.•Substantial challenges exist for fitting detailed models to data.•Conceptual and …

The computational and cognitive properties of neural systems are often thought to be
implemented in terms of their (stochastic) network dynamics. Hence, recovering the system …

Descriptive statistical models of neural responses generally aim to characterize the map**
from stimuli to spike responses while ignoring biophysical details of the encoding process …

Bernstein Series in Computational Neuroscience reflects the Bernstein Network's broad
research and teaching activities, including models of neural circuits and higher brain …

Sequential change-point detection from time series data is a common problem in many
neuroscience applications, such as seizure detection, anomaly detection, and pain …

Background Investigation of neural circuit functioning often requires statistical interpretation
of events in subthreshold electrophysiological recordings. This problem is non-trivial …

The rhythmic activity of motoneurons (MNs) that underlies locomotion in mammals is
generated by synaptic inputs from the locomotor network in the spinal cord. Thus, the …

Multi-dimensional stochastic differential equations (SDEs) are a powerful tool to describe
dynamics of phenomena that change over time. We focus on the parametric estimation of …