Pure Networks Network Magic Pro V5.5.9118.2 -BRD [mattlb0619] 64 Bit
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Networks are able to learn from input patterns, to store, or modify their connections in response to subsequent input patterns. Hence they can be used to explain the temporal structure of biological, cognitive and sensory data, which are intrinsically characterized by temporal relations. Networks are attractive models in many applications, because they combine the characteristics of feedforward and recurrent architectures, which can generate internal (short-term) and long-term memory. The NN-RM (Recurrent Modelling) ( ) is a weight-based approach to the neurophysiological problem of association of unipolar or multipolar neuronal populations. It has the advantage of allowing a wide range of weight values which are not required to fulfil local distance constraints. A population of cells is composed of a number of cells, each cell is connected to each other through their synaptic weight values. Each cell can be represented as a simple attractor, whose dynamics is characterized by the network weights and the properties of the input cells. The nature of the model is based on the Hopfield network model, where each input of the network can be considered as an external stimulus in an experiment, in this way the network can learn to associate input patterns with its states. The connections between the cells can be a type of weight variables. These connection weights can be learned or inherited from experience, in this way the network can also learn temporal patterns of complex stimuli. The mechanism can be applied to EEG recordings, which are intrinsically characterized by temporal relations, and as a consequence the potential for implementation in a model of the brain has been considered as promising.
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