Results 1  10
of
2,520,729
Knowledge Discovery and SelfOrganizing State Space Model
"... hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and selforganizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract essential inf ..."
Abstract
 Add to MetaCart
hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and selforganizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract essential
PAPER Surveys on Discovery ScienceKnowledge Discovery and SelfOrganizing State Space Model
"... SUMMARY A hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and selforganizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract ess ..."
Abstract
 Add to MetaCart
SUMMARY A hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and selforganizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract
A SelfOrganizing StateSpaceModel Approach for Parameter Estimation in HodgkinHuxleyType Models of Single Neurons
"... Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the val ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of wellestablished statistical inference methods. The particular method we used was Kitagawa’s selforganizing statespace model, which was applied on a number of Hodgkin
Dynamic Stochastic General Equilibrium Models In a Liquidity Trap and Selforganizing State Space Modeling
"... This paper estimates new Keynesian, dynamic stochastic general equilibrium models in a liquidity trap (the nonnegativity constraint on short term nominal interest rates) using the Monte Carlo particle filter, proposed by Kitagawa (1996) and Gordon et al. (1993), and a selforganizing state space mo ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
This paper estimates new Keynesian, dynamic stochastic general equilibrium models in a liquidity trap (the nonnegativity constraint on short term nominal interest rates) using the Monte Carlo particle filter, proposed by Kitagawa (1996) and Gordon et al. (1993), and a selforganizing state space
1 A SelfOrganizing StateSpaceModel Approach for Parameter Estimation in HodgkinHuxleyType Models of Single Neurons
"... There are several ways to introduce noise in the HodgkinHuxleytype neuron models as the ones we examined in this paper [1,2]. A quite common approach is to add a white noise term in the righthand side of the current conservation equation (which describes the evolution of the membrane potential in ..."
Abstract
 Add to MetaCart
There are several ways to introduce noise in the HodgkinHuxleytype neuron models as the ones we examined in this paper [1,2]. A quite common approach is to add a white noise term in the righthand side of the current conservation equation (which describes the evolution of the membrane potential
*Manuscript Click here to download Manuscript: manuscript.pdf 1 A SelfOrganizing StateSpaceModel Approach for Parameter Estimation in HodgkinHuxleyType Models of Single Neurons
"... Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the val ..."
Abstract
 Add to MetaCart
dynamicalsystems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of wellestablished statistical inference methods. The particular method we used was Kitagawa’s selforganizing statespace model, which was applied on a number of Hodgkin
Protocols for selforganization of a wireless sensor network
 IEEE Personal Communications
, 2000
"... We present a suite of algorithms for selforganization of wireless sensor networks, in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energyefficient routing, and formation ..."
Abstract

Cited by 519 (5 self)
 Add to MetaCart
We present a suite of algorithms for selforganization of wireless sensor networks, in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energyefficient routing
Impulses and Physiological States in Theoretical Models of Nerve Membrane
 Biophysical Journal
, 1961
"... ABSTRACT Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of nonlinear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model " has two variables of state, representing excitabi ..."
Abstract

Cited by 496 (0 self)
 Add to MetaCart
the 4dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.
Symbolic Model Checking: 10^20 States and Beyond
, 1992
"... Many different methods have been devised for automatically verifying finite state systems by examining stategraph models of system behavior. These methods all depend on decision procedures that explicitly represent the state space using a list or a table that grows in proportion to the number of st ..."
Abstract

Cited by 753 (40 self)
 Add to MetaCart
Many different methods have been devised for automatically verifying finite state systems by examining stategraph models of system behavior. These methods all depend on decision procedures that explicitly represent the state space using a list or a table that grows in proportion to the number
Results 1  10
of
2,520,729