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» State dependent computation using coupled recurrent networks
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150
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CVPR
1999
IEEE
16 years 6 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
BMCBI
2011
14 years 7 months ago
FFCA: a feasibility-based method for flux coupling analysis of metabolic networks
Background: Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (...
László Dávid, Sayed-Amir Mara...
117
Voted
ISMB
2000
15 years 5 months ago
Prediction of the Number of Residue Contacts in Proteins
Knowing the number of residue contacts in a protein is crucial for deriving constraints useful in modeling protein folding, protein structure, and/or scoring remote homology searc...
Piero Fariselli, Rita Casadio
JSAC
2008
82views more  JSAC 2008»
15 years 3 months ago
Integration of communication and control using discrete time Kuramoto models for multivehicle coordination over broadcast networ
Abstract-- This paper considers the integration of communication and control with respect to the task of coordinated heading control for a group of N vehicles. The heading control ...
Daniel J. Klein, Phillip Lee, Kristi A. Morgansen,...
JCNS
2007
89views more  JCNS 2007»
15 years 3 months ago
Synchronous and asynchronous bursting states: role of intrinsic neural dynamics
Brain signals such as local field potentials often display gamma-band oscillations (30–70 Hz) in a variety of cognitive tasks. These oscillatory activities possibly reflect sy...
Takashi Takekawa, Toshio Aoyagi, Tomoki Fukai