ArticleScientific reports2018
Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets.Cell reports methods · 2024Article
- Simulations approaching data: cortical slow waves in inferred models of the whole hemisphere of mouse.Communications biology · 2023Article
- Error-based or target-based? A unified framework for learning in recurrent spiking networks.PLoS computational biology · 2022Article
- Beyond AOPs: A Mechanistic Evaluation of NAMs in DART Testing.Frontiers in toxicology · 2022Article
- Target spike patterns enable efficient and biologically plausible learning for complex temporal tasks.PloS one · 2021Article
- Cytosolic PSD-95 interactor alters functional organization of neural circuits and AMPA receptor signaling independent of PSD-95 binding.Network neuroscience (Cambridge, Mass.) · 2021Article
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3 authors.
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Abstract
Inference methods are widely used to recover effective models from observed data. However, few studies attempted to investigate the dynamics of inferred models in neuroscience, and none, to our knowledge, at the network level. We introduce a principled modification of a widely used generalized linear model (GLM), and learn its structural and dynamic parameters from in-vitro spike data. The spontaneous activity of the new model captures prominent features of the non-stationary and non-linear dynamics displayed by the biological network, where the reference GLM largely fails, and also reflects fine-grained spatio-temporal dynamical features. Two ingredients were key for success. The first is a saturating transfer function: beyond its biological plausibility, it limits the neuron's information transfer, improving robustness against endogenous and external noise. The second is a super-Poisson spikes generative mechanism; it accounts for the undersampling of the network, and allows the model neuron to flexibly incorporate the observed activity fluctuations.
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