Inferring and validating mechanistic models of neural microcircuits based on spike-train data
It is difficult to fit mechanistic, biophysically constrained circuit models to spike train data from in vivo extracellular recordings. Here the authors present analytical methods that enable efficient parameter estimation for integrate-and-fire circuit models and inference of the underlying connect...
Guardado en:
Autores principales: | Josef Ladenbauer, Sam McKenzie, Daniel Fine English, Olivier Hagens, Srdjan Ostojic |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2019
|
Materias: | |
Acceso en línea: | https://doaj.org/article/85a037d778554f659901d36b5d3fc974 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
por: Bernhard Nessler, et al.
Publicado: (2013) -
Supervised learning in spiking neural networks with FORCE training
por: Wilten Nicola, et al.
Publicado: (2017) -
SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
por: Fangxin Liu, et al.
Publicado: (2021) -
Inhibitory microcircuits for top-down plasticity of sensory representations
por: Katharina Anna Wilmes, et al.
Publicado: (2019) -
A convolutional neural network for estimating synaptic connectivity from spike trains
por: Daisuke Endo, et al.
Publicado: (2021)