Reconstructing neuronal circuitry from parallel spike trains
Current techniques have enabled the simultaneous collection of spike train data from large numbers of neurons. Here, the authors report a method to infer the underlying neural circuit connectivity diagram based on a generalized linear model applied to spike cross-correlations between neurons.
Guardado en:
Autores principales: | Ryota Kobayashi, Shuhei Kurita, Anno Kurth, Katsunori Kitano, Kenji Mizuseki, Markus Diesmann, Barry J. Richmond, Shigeru Shinomoto |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2019
|
Materias: | |
Acceso en línea: | https://doaj.org/article/1139b3b3aec44e7da4d4083a6c74e53b |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A convolutional neural network for estimating synaptic connectivity from spike trains
por: Daisuke Endo, et al.
Publicado: (2021) -
Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization
por: Jian K. Liu, et al.
Publicado: (2017) -
An artificial spiking quantum neuron
por: Lasse Bjørn Kristensen, et al.
Publicado: (2021) -
Drosulfakinin signaling in fruitless circuitry antagonizes P1 neurons to regulate sexual arousal in Drosophila
por: Shunfan Wu, et al.
Publicado: (2019) -
Collective and synchronous dynamics of photonic spiking neurons
por: Takahiro Inagaki, et al.
Publicado: (2021)