Information om | Engelska ordet HEBBIAN

Exempel på hur man kan använda HEBBIAN i en mening

• Hebbian theory is a neuropsychological theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell.
• Since memories are postulated to be represented by vastly interconnected neural circuits in the brain, synaptic plasticity is one of the important neurochemical foundations of learning and memory (see Hebbian theory).
• Taylor suggested that if synapses were strengthened for which a presynaptic spike occurred just before a postsynaptic spike more often than the reverse (Hebbian learning), while with the opposite timing or in the absence of a closely timed presynaptic spike, synapses were weakened (anti-Hebbian learning), the result would be an informationally efficient recoding of input patterns.
• Boltzmann machines are theoretically intriguing because of the locality and Hebbian nature of their training algorithm (being trained by Hebb's rule), and because of their parallelism and the resemblance of their dynamics to simple physical processes.
• Frank Rosenblatt studied "close-loop cross-coupled perceptrons", which are 3-layered perceptron networks whose middle layer contains recurrent connections that change by a Hebbian learning rule.
• In neural networks, Kosko introduced the unsupervised technique of differential Hebbian learning, sometimes called the "differential synapse," and most famously the BAM or bidirectional associative memory.
• Donald Olding Hebb (MA, 1932) – father of cognitive psychobiology; pioneer in artificial intelligence; developed concept of Hebbian learning.
• Although Hebbian forms of plasticity, such as long-term potentiation and long-term depression occur rapidly, homeostatic plasticity (which relies on protein synthesis) can take hours or days.
• Kosko and Dickerson (Dickerson & Kosko, 1994) suggested the Differential Hebbian Learning (DHL) to train FCM.
• The generalized Hebbian algorithm (GHA), also known in the literature as Sanger's rule, is a linear feedforward neural network for unsupervised learning with applications primarily in principal components analysis.
• Specifically, Hebbian theory (which in many regards is the neuroscience equivalent of original associationist theories) is one explanation of synaptic plasticity (Hebb, 1949).

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