Marius Pachitariu

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SUMMARY Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a determin-istic spiking network model(More)
We introduce the Recurrent Generalized Linear Model (R-GLM), an extension of GLMs based on a compact representation of the spiking history through a linear recurrent neural network. R-GLMs match the predictive likelihood of Linear Dynamical Systems (LDS) with linear-Gaussian observations. We also address a disadvantage of GLMs, including the R-GLM, that(More)
The primary mode of information transmission in neural networks is unknown: is it a rate code or a timing code? Assuming that presynaptic spike trains are stochastic and a rate code is used, probabilistic models of spiking can reveal properties of the neural computation performed at the level of single neurons. Here we show that depending on the(More)
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