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Pairs of neurons in brain networks often share much of the input they receive from other neurons. Due to essential nonlinearities of the neuronal dynamics, the consequences for the correlation… Click to show full abstract
Pairs of neurons in brain networks often share much of the input they receive from other neurons. Due to essential nonlinearities of the neuronal dynamics, the consequences for the correlation of the output spike trains are generally not well understood. Here we analyze the case of two leaky integrate-and-fire neurons using an approach which is nonperturbative with respect to the degree of input correlation. Our treatment covers both weakly and strongly correlated dynamics, generalizing previous results based on linear response theory.
Journal Title: Physical Review E
Year Published: 2017
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