International audienceLearning or memory formation are associated with the strengthening of the synaptic connections between neurons according to a pattern reflected by the input. According to this theory a retained memory sequence is associated to a dynamic pattern of the associated neural circuit. In this work we consider a class of network neuron models, known as Hopfield networks, with a learning rule which consists of transforming an information string to a coupling pattern. Within this class of models we study dynamic patterns, known as robust heteroclinic cycles, and establish a tight connection between their existence and the structure of the coupling
Attractor neural networks such as the Hopfield model can be used to model associative memory. An eff...
Hopfield neural networks are a possible basis for modelling associative memory in living organisms. ...
SummaryThe ability to associate some stimuli while differentiating between others is an essential ch...
International audienceLearning or memory formation are associated with the strengthening of the syna...
International audienceWe review and extend the previous work where a model was introduced for Hopfie...
Cyclic patterns of neuronal activity are ubiquitous in animal nervous systems, and partially respons...
The synchronization of oscillatory activity in networks of neural networks is usually implemented th...
Best Paper AwardInternational audienceNeuronal models of associative memories are recurrent networks...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
Hopfield nets are among the most commonly used models in machine learning and neuroscience today. In...
In neural networks, two specific dynamical behaviours are well known: 1) Networks naturally find pat...
The Hopfield network (Hopfield, 1982,1984) provides a simple model of an associative memory in a neu...
The synchronization of oscillatory activity in neural networks is usually implemented by coupling th...
Copyright © by Society for Industrial and Applied Mathematics. Unauthorized reproduction of this art...
Attractor neural networks such as the Hopfield model can be used to model associative memory. An eff...
Hopfield neural networks are a possible basis for modelling associative memory in living organisms. ...
SummaryThe ability to associate some stimuli while differentiating between others is an essential ch...
International audienceLearning or memory formation are associated with the strengthening of the syna...
International audienceWe review and extend the previous work where a model was introduced for Hopfie...
Cyclic patterns of neuronal activity are ubiquitous in animal nervous systems, and partially respons...
The synchronization of oscillatory activity in networks of neural networks is usually implemented th...
Best Paper AwardInternational audienceNeuronal models of associative memories are recurrent networks...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
Hopfield nets are among the most commonly used models in machine learning and neuroscience today. In...
In neural networks, two specific dynamical behaviours are well known: 1) Networks naturally find pat...
The Hopfield network (Hopfield, 1982,1984) provides a simple model of an associative memory in a neu...
The synchronization of oscillatory activity in neural networks is usually implemented by coupling th...
Copyright © by Society for Industrial and Applied Mathematics. Unauthorized reproduction of this art...
Attractor neural networks such as the Hopfield model can be used to model associative memory. An eff...
Hopfield neural networks are a possible basis for modelling associative memory in living organisms. ...
SummaryThe ability to associate some stimuli while differentiating between others is an essential ch...