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 …

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 …

In the present paper we propose a novel method for the identification and modeling of
neural networks using extracellular spike recordings. We create a deterministic model of the …

Estimating the connectivity strength based on the signals observed at each node of a
network is an important task in neural network analysis. One notable example is estimating …

Repeating patterns of precisely timed activity across a group of neurons (called frequent
episodes) are indicative of networks in the underlying neural tissue. This letter develops …

Most of the existing methods to extract information about the interactions within a network of
dynamical systems starting from measured data work well for networks with a limited number …

We propose a method to extract connectivity between neurons for extracellularly recorded
multiple spike trains. The method removes pseudo-correlation caused by propagation of …

The activity of cortical neurons is determined by the input they receive from presynaptic
neurons. Many previous studies have investigated how specific aspects of the statistics of …

The main result of this thesis is the development of a novel connectivity estimation method,
called Total Spiking Probability Edges (TSPE). Based on cross-correlation and edge filtering …

The study of networks has become increasingly important in many areas ranging from
physics to biology. Knowledge of connectivity in a network is useful for understanding its …