Cargando…
Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task
Primates can richly parse sensory inputs to infer latent information. This ability is hypothesized to rely on establishing mental models of the external world and running mental simulations of those models. However, evidence supporting this hypothesis is limited to behavioral models that do not emul...
Autores principales: | Rajalingham, Rishi, Piccato, Aída, Jazayeri, Mehrdad |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532407/ https://www.ncbi.nlm.nih.gov/pubmed/36195614 http://dx.doi.org/10.1038/s41467-022-33581-6 |
Ejemplares similares
-
Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes
por: Nayebi, Aran, et al.
Publicado: (2023) -
Engineering recurrent neural networks from task-relevant manifolds and dynamics
por: Pollock, Eli, et al.
Publicado: (2020) -
Characterization of neurons in the primate medial intraparietal area reveals a joint representation of intended reach direction and amplitude
por: Rajalingham, Rishi, et al.
Publicado: (2017) -
Predicting change: Approximate inference under explicit representation of temporal structure in changing environments
por: Marković, Dimitrije, et al.
Publicado: (2019) -
Can Implicit or Explicit Time Processing Impact Numerical Representation? Evidence From a Dual Task Paradigm
por: Di Bono, Maria Grazia, et al.
Publicado: (2020)