ArticleJournal of computational neuroscience2011
Fast inference of interactions in assemblies of stochastic integrate-and-fire neurons from spike recordings.
Article in Journal of computational neuroscience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Inferring and validating mechanistic models of neural microcircuits based on spike-train data.Nature communications · 2019Article
- Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology.Journal of computational neuroscience · 2018Article
- Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity.Network neuroscience (Cambridge, Mass.) · 2017Article
- Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings.Journal of computational neuroscience · 2017Article
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Authors and funding
2 authors.
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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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