ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2016
Inference and Decoding of Motor Cortex Low-Dimensional Dynamics via Latent State-Space Models.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
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- Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling.Scientific reports · 2024Article
- Neural manifolds carry reactivation of phonetic representations during semantic processing.bioRxiv : the preprint server for biology · 2024Article
- Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.bioRxiv : the preprint server for biology · 2024Article
- Dynamical flexible inference of nonlinear latent factors and structures in neural population activity.Nature biomedical engineering · 2024Article
- Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas.Frontiers in computational neuroscience · 2024Review
- Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.Frontiers in computational neuroscience · 2023Article
- The science and engineering behind sensitized brain-controlled bionic hands.Physiological reviews · 2022Review
- Cortical Control of Virtual Self-Motion Using Task-Specific Subspaces.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior.Nature communications · 2021Article
- Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification.Nature neuroscience · 2021Article
- Neural field models for latent state inference: Application to large-scale neuronal recordings.PLoS computational biology · 2019Article
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Abstract
Motor cortex neuronal ensemble spiking activity exhibits strong low-dimensional collective dynamics (i.e., coordinated modes of activity) during behavior. Here, we demonstrate that these low-dimensional dynamics, revealed by unsupervised latent state-space models, can provide as accurate or better reconstruction of movement kinematics as direct decoding from the entire recorded ensemble. Ensembles of single neurons were recorded with triple microelectrode arrays (MEAs) implanted in ventral and dorsal premotor (PMv, PMd) and primary motor (M1) cortices while nonhuman primates performed 3-D reach-to-grasp actions. Low-dimensional dynamics were estimated via various types of latent state-space models including, for example, Poisson linear dynamic system (PLDS) models. Decoding from low-dimensional dynamics was implemented via point process and Kalman filters coupled in series. We also examined decoding based on a predictive subsampling of the recorded population. In this case, a supervised greedy procedure selected neuronal subsets that optimized decoding performance. When comparing decoding based on predictive subsampling and latent state-space models, the size of the neuronal subset was set to the same number of latent state dimensions. Overall, our findings suggest that information about naturalistic reach kinematics present in the recorded population is preserved in the inferred low-dimensional motor cortex dynamics. Furthermore, decoding based on unsupervised PLDS models may also outperform previous approaches based on direct decoding from the recorded population or on predictive subsampling.
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