ReviewJournal of physiology, Paris2016
From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.
Review in Journal of physiology, Paris, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 36 citations in OpenAlex.
- Adaptive modeling and inference of higher-order coordination in neuronal assemblies: A dynamic greedy estimation approach.PLoS computational biology · 2024Article
- Neural manifolds carry reactivation of phonetic representations during semantic processing.bioRxiv : the preprint server for biology · 2024Article
- Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas.Frontiers in computational neuroscience · 2024Review
- Variational log-Gaussian point-process methods for grid cells.Hippocampus · 2023Article
- Adaptive modeling and inference of higher-order coordination in neuronal assemblies: a dynamic greedy estimation approach.bioRxiv : the preprint server for biology · 2023Article
- Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects.Entropy (Basel, Switzerland) · 2022Article
- Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodes.Entropy (Basel, Switzerland) · 2021Article
- Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data.Statistics and computing · 2021Article
- Neural field models for latent state inference: Application to large-scale neuronal recordings.PLoS computational biology · 2019Article
- Stability of stochastic finite-size spiking-neuron networks: Comparing mean-field, 1-loop correction and quasi-renewal approximations.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
- On the nature and use of models in network neuroscience.Nature reviews. Neuroscience · 2018Review
- COMPASS: An Open-Source, General-Purpose Software Toolkit for Computational Psychiatry.Frontiers in neuroscience · 2018Article
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Abstract
This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.