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

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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.
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):
Gap Junctions:
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]; Schiff, Steven [sschiff at]; Gluckman, Bruce [BruceGluckman at];
Search NeuronDB for information about:  Acetylcholine; Norephinephrine; Gaba; Serotonin;
%this function holds parameters of the DB model of sleep, duplicated from:
%Fleshner, Booth, Forger, Diniz Behn, Philos Transact A
%Math Phys Eng Sci. 2011 Oct 13;369(1952):3855-83.
function P= OriginalFBFDParams

% neuro paramters (all in order of N,S,G,AR,AWR)
P.cgamma=[5 5 4 3 3 4]; %
P.ctau=[25 25 10 10 10 10];

P.gALC=3.5; P.gNLC=1.5; P.gGLC=1.5;
P.gADR=3.5; P.gSDR=1.5; P.gGDR=1.5;
P.gAR=2.5; P.gNR=3.5; P.gSR=3.5; P.gGR=1.25;
P.gAWR=1; P.gGWR=1.7;
P.gNVLPO=2; P.gSVLPO=2; P.gGVLPO=0.5;
P.gASCN=0.2; P.gSSCN=0.2; P.gGSCNLC=4;

%firing rate parameters (LC,DR,VLPO,R,WR)
P.Ftau=[25 25 10 1 10 0.5];
P.Fmax=[6.5 6.5 5 5 5 8];
P.Falpha=[0.75 0.75 0.25 0.25 0.25 1.5];

%these are different
P.betaLC=-1.85; P.betaDR=-1.85; P.betaR=-0.82; P.betaWR=-0.2; P.betaSCN=0.25;
%we've left beta out because it is dependent on h

%homeostatic sleep constants
P.thetaW=3; P.tauhs=200; P.tauhw=700;