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Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density

Autor
Closas, P.; Guillamon, A.
Tipus d'activitat
Article en revista
Revista
Eurasip journal on advances in signal processing
Data de publicació
2017-12-01
Número
65
Pàgina inicial
1
Pàgina final
22
DOI
https://doi.org/10.1186/s13634-017-0499-3 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/108193 Obrir en finestra nova
URL
https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-017-0499-3 Obrir en finestra nova
Resum
This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain’s connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) are at the core of our study. The sole observation available are noisy, sampled voltage traces obtained from intracellular recordings. We design algorithms and inference methods using the tools provided by...
Paraules clau
Conductance-based Model, Inference And Learning, Intracellular Recording, Particle Filtering, Spiking Neuron, State-space Models, Synaptic Conductance Estimation
Grup de recerca
SD - Sistemes Dinàmics

Participants