Fast inference of interactions in assemblies of stochastic integrate-and-fire neurons from spike recordings |
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Authors: | Remi Monasson Simona Cocco |
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Institution: | 1.Laboratoire de Physique Théorique de l’ENS,CNRS & UPMC,Paris,France;2.Laboratoire de Physique Statistique de l’ENS,CNRS & UPMC,Paris,France;3.The Simons Center for Systems Biology,Institute for Advanced Study,Princeton,USA |
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Abstract: | We present two Bayesian procedures to infer the interactions and external currents in an assembly of stochastic integrate-and-fire
neurons from the recording of their spiking activity. The first procedure is based on the exact calculation of the most likely
time courses of the neuron membrane potentials conditioned by the recorded spikes, and is exact for a vanishing noise variance
and for an instantaneous synaptic integration. The second procedure takes into account the presence of fluctuations around
the most likely time courses of the potentials, and can deal with moderate noise levels. The running time of both procedures
is proportional to the number S of spikes multiplied by the squared number N of neurons. The algorithms are validated on synthetic data generated by networks with known couplings and currents. We also
reanalyze previously published recordings of the activity of the salamander retina (including from 32 to 40 neurons, and from
65,000 to 170,000 spikes). We study the dependence of the inferred interactions on the membrane leaking time; the differences
and similarities with the classical cross-correlation analysis are discussed. |
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Keywords: | |
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