Add to ORKGWhen we model a higher order functions, such as learning and memory, we face a difficulty of comparing neural activities with hidden variables that depend on the history of sensory and motor signals and the dynamics of the network. Here, we propose novel method for estimating hidden variables of a learning agent, such as connection weights from sequences of observable variables. Bayesian estimation is a method to estimate the posterior probability of hidden variables from observable data sequence using a dynamic model of hidden and observable variables. In this paper, we apply particle filter for estimating internal parameters and metaparameters of a reinforcement learning model. We verified the effectiveness of the method using both artifi...
The computational task of continuous-time state estimation, nonlinear filtering and identification, ...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
textA primary goal in systems neuroscience is to understand how neural spike responses encode inform...
The robust estimation of dynamical hidden features, such as the position of prey, based on sensory i...
The robust estimation of dynamically changing features, such as the position of prey, is one of the ...
Abstract—We consider a parameter estimation problem for a Hidden Markov Model in the framework of pa...
This paper develops a toolkit of inference and forecasting methods for a large class of nonlinear in...
The internal representations of 'learned' knowledge in neural networks are still poorly understood, ...
The internal representations of 'learned' knowledge in neural networks are still poorly understood, ...
Recent work on intelligent tutoring systems has used Bayesian networks to model students ’ acquisiti...
Abstract—In a large number of applications, engineers have to estimate a function linked to the stat...
The human brain effortlessly solves problems that still pose a challenge for modern computers, such ...
Abstract. A wide variety of function approximation schemes have been applied to reinforcement learni...
We consider the inverse reinforcement learning problem, that is, the problem of learning from, and t...
Accurate characterizations of behavior during learning experiments are essential for understanding t...
The computational task of continuous-time state estimation, nonlinear filtering and identification, ...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
textA primary goal in systems neuroscience is to understand how neural spike responses encode inform...
The robust estimation of dynamical hidden features, such as the position of prey, based on sensory i...
The robust estimation of dynamically changing features, such as the position of prey, is one of the ...
Abstract—We consider a parameter estimation problem for a Hidden Markov Model in the framework of pa...
This paper develops a toolkit of inference and forecasting methods for a large class of nonlinear in...
The internal representations of 'learned' knowledge in neural networks are still poorly understood, ...
The internal representations of 'learned' knowledge in neural networks are still poorly understood, ...
Recent work on intelligent tutoring systems has used Bayesian networks to model students ’ acquisiti...
Abstract—In a large number of applications, engineers have to estimate a function linked to the stat...
The human brain effortlessly solves problems that still pose a challenge for modern computers, such ...
Abstract. A wide variety of function approximation schemes have been applied to reinforcement learni...
We consider the inverse reinforcement learning problem, that is, the problem of learning from, and t...
Accurate characterizations of behavior during learning experiments are essential for understanding t...
The computational task of continuous-time state estimation, nonlinear filtering and identification, ...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
textA primary goal in systems neuroscience is to understand how neural spike responses encode inform...