A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model that is based on principles of high dimensional dynamical systems in combination with statistical learning theory. It can be implemented on generic evolved or found recurrent circuitr
We employ an efficient method using Bayesian and linear classifiers for analyzing the dynamics of in...
Humans are able to form internal representations of the information they process – a capability wh...
Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of i...
A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from ...
A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from ...
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a...
We investigate generic models for cortical microcircuits, i.e., recurrent circuits of integrate-and-...
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a...
We investigate generic models for cortical microcircuits, i.e. recurrent circuits of integrate-and f...
It has previously been shown that generic cortical microcircuit models can perform complex real-time...
It has previously been shown that generic cortical microcircuit models can perform complex real-time...
AbstractComplex real-time computations on multi-modal time-varying input streams are carried out by ...
2 Temporal integration of information and prediction of future sensory inputs are assumed to be impo...
We employ an efficient method using Bayesian and linear classifiers for analyzing the dynamics of i...
Complex real-time computations on multi-modal time-varying input streams are carried out by generic ...
We employ an efficient method using Bayesian and linear classifiers for analyzing the dynamics of in...
Humans are able to form internal representations of the information they process – a capability wh...
Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of i...
A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from ...
A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from ...
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a...
We investigate generic models for cortical microcircuits, i.e., recurrent circuits of integrate-and-...
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a...
We investigate generic models for cortical microcircuits, i.e. recurrent circuits of integrate-and f...
It has previously been shown that generic cortical microcircuit models can perform complex real-time...
It has previously been shown that generic cortical microcircuit models can perform complex real-time...
AbstractComplex real-time computations on multi-modal time-varying input streams are carried out by ...
2 Temporal integration of information and prediction of future sensory inputs are assumed to be impo...
We employ an efficient method using Bayesian and linear classifiers for analyzing the dynamics of i...
Complex real-time computations on multi-modal time-varying input streams are carried out by generic ...
We employ an efficient method using Bayesian and linear classifiers for analyzing the dynamics of in...
Humans are able to form internal representations of the information they process – a capability wh...
Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of i...