Our ability to collect large amounts of data from many cells has been paralleled by the
development of powerful statistical models for extracting information from this data. Here we …

We study the statistics of spike trains of simultaneously recorded grid cells in freely behaving
rats. We evaluate pairwise correlations between these cells and, using a maximum entropy …

Maximum entropy models can be inferred from large datasets to uncover how collective
dynamics emerge from local interactions. Here, such models are employed to investigate …

Online public opinions refer to the attitude of the public towards certain events or topics in
social media and have informative significance for social governance and the formulation of …

Williams–Beuren syndrome (WBS) is a rare neurodevelopmental disorder characterized by
moderate intellectual disability and learning difficulties alongside behavioral abnormalities …

Recurrent spiking neural networks (RSNN) in the brain learn to perform a wide range of
perceptual, cognitive and motor tasks very efficiently in terms of energy consumption and …

We consider the problem of inferring a causality structure from multiple binary time series by
using the kinetic Ising model in datasets where a fraction of observations is missing. Inspired …

Much of contemporary systems biology owes its success to the abstraction of a network, the
idea that diverse kinds of molecular, cellular, and organismal species and interactions can …

Binary random variables are the building blocks used to describe a large variety of systems,
from magnetic spins to financial time series and neuron activity. In statistical physics the …

With our ability to record more neurons simultaneously, making sense of these data is a
challenge. Functional connectivity is one popular way to study the relationship of multiple …