The organization of behavior

By D. Hebb. 1988

Subfields: Cognitive Science & Psychology

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, methodology): “tion, neural networks models have stressed the importance of learning. There are many learning algorithms for neural networks, including the perceptron algorithm (Rosenblatt, 1958), Hebbian learning (Hebb, 1949), the BCM rule (Bienenstock, Cooper, & Munro, 1982), backpropagation (D. Rumelhart, H”
Local Aggregation for Unsupervised Learning of Visual Embeddings (2019, neutral): “this algorithm learns and justifying key parameter choices. 2. Related Work Unsupervised learning methods span a very broad spectrum of approaches going back to the roots of artiﬁcial neural networks [53,39,2,58,27,41,28,30,25], and are too numerous to fully review here. However, several recent work”
Continual Lifelong Learning with Neural Networks: A Review (2018, background): “ural malleability at the level of cells and circuits (seePower & Schlaggar(2016) a survey). For a stable continuous lifelong process, two types of plasticity are required: (i) Hebbian plasticity (Hebb 1949) for positive feedback instability, and (ii) compensatory homeostatic plasticity which sta”
Modeling the spontaneous reactivation of experience‐specific hippocampal cell assembles during sleep (1996, background): “Given these basic assumptions, a simple Hebbian learning rule (Hebb, 1949) leads to a weight matrix in which the strength of the connections between two cells is proportional to the overlap of their place fields.”
Attention-Based Structural-Plasticity (2019, background): “d post synaptic neurons are important (relative to the task that is being learned), where the importance of the neurons are identiﬁed via Equation (2). This is the basic idea behind Hebbian learning (Hebb, 1961). Hebbian learning of importance parameters, however, suffers from the severe problem of ”