Abstract. We examined neural networks built of several hundred Hodgkin-Huxley neurons. The main aim of the research described below was to simulate memory ...
Cloning of information and modification of the weight of connections between neurons are used as the basic principles for learning and recognition processes.
We examined neural networks built of several hundred Hodgkin-Huxley neurons. The main aim of the research described below was to simulate memory processes ...
Hebbian Theory is defined as a concept proposed by Donald Hebb in 1949, stating that neuronal connections can be remodeled by experience.
The theory attempts to explain associative or Hebbian learning, in which simultaneous activation of cells leads to pronounced increases in synaptic strength ...
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We argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations ...
Hebbian learning is a model for long-term potentiation in neurons, in which weights are increased when the input and output are simultaneously active.
We developed a scalable, deep network architecture for predictive coding that is trained using a gated Hebbian learning rule and mimics the feedforward and ...
This suggests that disparity encoding in the early visual system does not necessarily need supervised training, and an unsupervised feedforward Hebbian ...
Convolutional neural networks (CNN) are today the best model at hand to mimic the object recognition capabilities of the human visual system.
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