Add to ORKGMost of the current neural networks use models which have only tenuous connections to the biological neural systems on which they purport to be based t and negligible input from the neuroscience/biophysics communities. This pape
We outline the main models and developments in the broad field of artificial neural networks (ANN). ...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
An Artificial Neural Network (ANN) is an information processing model that is encouraged by the way ...
Neural Network models have received increased attention in the recent years. Aimed at achieving huma...
Human memory is associative and emerges from the behaviour of neurons. Two models, based on commonly...
none3noWe present the main aspects of mathematical models for computational neuroscience, with empha...
Associative learning involves the encoding of relationships between events, for example, between two...
Objective. In the theoretical framework of predictive coding and active inference, the brain can be ...
Introduction The associative memory is one of the fundamental algorithms of information processing ...
Artificial neuron network (ANN) is non linear mapping structures based on the function of human inte...
A learning rule for stochastic neural networks is described, which corresponds to biological neural ...
Currently used neural networks employ mostly simple neuron models that greatly differ from the "real...
An associative neural network (ASNN) is an ensemble-based method inspired by the function and struct...
A biological neurone receives inputs from many sources, combines and presents them as a non-linear o...
Currently neural networks are used in many different domains. But are neural networks also suitable ...
We outline the main models and developments in the broad field of artificial neural networks (ANN). ...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
An Artificial Neural Network (ANN) is an information processing model that is encouraged by the way ...
Neural Network models have received increased attention in the recent years. Aimed at achieving huma...
Human memory is associative and emerges from the behaviour of neurons. Two models, based on commonly...
none3noWe present the main aspects of mathematical models for computational neuroscience, with empha...
Associative learning involves the encoding of relationships between events, for example, between two...
Objective. In the theoretical framework of predictive coding and active inference, the brain can be ...
Introduction The associative memory is one of the fundamental algorithms of information processing ...
Artificial neuron network (ANN) is non linear mapping structures based on the function of human inte...
A learning rule for stochastic neural networks is described, which corresponds to biological neural ...
Currently used neural networks employ mostly simple neuron models that greatly differ from the "real...
An associative neural network (ASNN) is an ensemble-based method inspired by the function and struct...
A biological neurone receives inputs from many sources, combines and presents them as a non-linear o...
Currently neural networks are used in many different domains. But are neural networks also suitable ...
We outline the main models and developments in the broad field of artificial neural networks (ANN). ...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
An Artificial Neural Network (ANN) is an information processing model that is encouraged by the way ...