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Modelling latent structures in neural activity to better predict behaviour

Nonlinear latent factors and latent structures in the activity of neural populations can be computationally modelled to enable flexible inference and to better predict neural activity and behaviour.
Published in Neuroscience
Go to the profile of Pep Pàmies
Pep Pàmies
Chief Editor, Nature Biomedical Engineering, Nature Research
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Nature
Nature Nature

Dynamical flexible inference of nonlinear latent factors and structures in neural population activity - Nature Biomedical Engineering

Nonlinear latent factors and latent structures in the activity of neural populations can be computationally modelled to enable flexible inference and to better predict neural activity and behaviour.

The cover illustrates that latent factors and latent structures in the activity of neural populations can be computationally modelled to better predict neural activity and behaviour.

See Abbaspourazad et al.

Image: Ella Marushchenko and Ekaterina Zvorykina (Ella Maru Studio, Inc.). Cover design: Alex Wing.
Go to the profile of Pep Pàmies
Pep Pàmies
Chief Editor, Nature Biomedical Engineering, Nature Research

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Computational Neuroscience
Life Sciences > Biological Sciences > Neuroscience > Computational Neuroscience

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