LFADS - Latent Factor Analysis via Dynamical Systems
Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data should be analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer latent dynami...
Gardado en:
Main Authors: | David Sussillo, Rafał Józefowicz, L. F. Abbott, Chethan Pandarinath |
---|---|
Formato: | Pré-impressão |
Idioma: | inglés |
Publicado: |
2016
|
Acceso en liña: | https://doi.org/10.48550/arxiv.1608.06315 |
Tags: |
Engadir etiqueta
Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!
|
Títulos similares
-
Inferring single-trial neural population dynamics using sequential auto-encoders
por: Chethan Pandarinath, et al.
Publicado: (2018) -
Inferring single-trial neural population dynamics using sequential auto-encoders
por: Chethan Pandarinath, et al.
Publicado: (2017) -
Inferring single-trial neural population dynamics using sequential auto-encoders
por: Pandarinath, Chethan, et al.
Publicado: (2018) -
Latent Factors and Dynamics in Motor Cortex and Their Application to Brain–Machine Interfaces
por: Chethan Pandarinath, et al.
Publicado: (2018) -
Latent Factors and Dynamics in Motor Cortex and Their Application to Brain–Machine Interfaces
por: Pandarinath, Chethan, et al.
Publicado: (2018)