Despite the progresses of statistical and machine learning techniques, simultaneous
recordings from many neurons hide important information and the connections …

The dynamics of a neuron are influenced by the connections with the network where it lies.
Recorded spike trains exhibit patterns due to the interactions between neurons. However …

Simultaneous recordings from groups of neurons request to improve models. To switch from
single unit description to multivariate models describing the coding activity of two or more …

Recent technological advances have led to an unprecedented increase in the volume and
detail of neuroscientific data, creating significant challenges for their processing and …

Temporally, precise correlations between simultaneously recorded neurons have been
interpreted as signatures of cell assemblies, ie, groups of neurons that form processing …

The simultaneous recording of the activity of many neurons poses challenges for
multivariate data analysis. Here, we propose a general scheme of reconstruction of the …

We develop a method from semiparametric statistics (Cox, 1972) for the purpose of tracking
links and connection strengths over time in a neuronal network from spike train data. We …

To address the issue of extracting useful information from large data-set of large scale
networks of neurons, we propose an algorithm that involves both algebraic-statistical and …

The influential paper by Galves and Locherbach (2013), where a network of spiking neurons
is modelled as a system of interacting point processes, has led naturally to the question of …

One major challenge in neuroscience is the identification of interrelations between signals
reflecting neural activity and how information processing occurs in the neural circuits. At the …