Reconstrucing sleep dynamics with data assimilation (Sedigh-Sarvestani et al., 2012)

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Accession:146554
We have developed a framework, based on the unscented Kalman filter, for estimating hidden states and parameters of a network model of sleep. The network model includes firing rates and neurotransmitter output of 5 cell-groups in the rat brain.
Reference:
1 . Sedigh-Sarvestani M, Schiff SJ, Gluckman BJ (2012) Reconstructing mammalian sleep dynamics with data assimilation. PLoS Comput Biol 8:e1002788 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Acetylcholine; Norephinephrine; Gaba; Serotonin;
Simulation Environment: MATLAB;
Model Concept(s): Oscillations; Parameter Fitting; Tutorial/Teaching; Sleep; unscented Kalman filter;
Implementer(s): Sedigh-Sarvestani, Madineh [m.sedigh.sarvestani at gmail.com]; Schiff, Steven [sschiff at psu.edu]; Gluckman, Bruce [BruceGluckman at psu.edu];
Search NeuronDB for information about:  Acetylcholine; Norephinephrine; Gaba; Serotonin;
 
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Figure Code
Figure 6_Parameter Estimation SCN
figure6.m
                            
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