Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using Gaussian mixture Kalman filtering

Frontiers in Computational Neuroscience

Volumn , Issue SEP, 2013, Pages

  Lankarany, M ^a,b   Zhu, W P ^a   Swamy, M N S ^a   Toyoizumi, Taro ^b,c  

^a Dept of Electrical and Computer Engineering   (Canada)

^b RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^c TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Excitatory and inhibitory synaptic inputs; Gaussian mixture Kalman filtering; Kalman filtering; Synaptic conductance

Indexed keywords

CONVENTIONAL TECHNIQUES; EXPECTATION-MAXIMIZATION ALGORITHMS; GAUSSIAN MIXTURE MODEL; GAUSSIAN MIXTURES; INHIBITORY SYNAPTIC INPUTS; KALMAN-FILTERING; LINEAR REGRESSION TECHNIQUES; SYNAPTIC CONDUCTANCE;

BRAIN; CHANNEL ESTIMATION; ESTIMATION; GAUSSIAN DISTRIBUTION; IMAGE SEGMENTATION; ITERATIVE METHODS; KALMAN FILTERS;

ALGORITHMS;

ALGORITHM; ARTICLE; CONTROLLED STUDY; EXCITATORY SYNAPTIC CONDUCTANCE; GAUSSIAN MIXTURE KALMAN FILTERING; INHIBITORY SYNAPTIC CONDUCTANCE; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MEMBRANE POTENTIAL; SIGNAL NOISE RATIO; STATISTICAL DISTRIBUTION; STIMULUS RESPONSE; SYNAPTIC POTENTIAL;

EID: 84884687809     PISSN: 16625188     EISSN: None     Source Type: Journal    
DOI: 10.3389/fncom.2013.00109     Document Type: Article

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