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Statistical models of neural activity are at the core of the field of modern computational neuroscience. The activity of single neurons has been modeled to successfully explain dependencies of neural dynamics to its own spiking history, to external stimuli ...
Since the seminal work of Watts in the late 90s [1], graph-theoretic analyses have been performed on many complex dynamic networks, including brain structures [2]. Most studies have focused on functional connectivity defined between whole brain regions, us ...
The coordinated, collective spiking activity of neuronal populations encodes and processes information. One approach towards understanding such population based computation is to fit statistical models to simultaneously recorded spike trains and use these ...
Multiple types of measures have been developed to measure the similarity between two spike trains. These were extensively used to classify neuron responses according to stimuli and to validate mathematical models that predict the spike times. Here we analy ...
Generalized Linear Models (GLMs) are an increasingly popular framework for modeling neural spike trains. They have been linked to the theory of stochastic point processes and researchers have used this relation to assess goodness-of-fit using methods from ...