Hebb rule

While we have shown that the Hebb rule (equation 10.19) yields the desired dynamical attractors at tlu local minima of the energy function (equation 10.9), we have not shown that the.,se attractors arc in fact the only ones possible in this system. In fact, they are not, and spurious attractors are also possible. 

Inserting the Hebb rule (equation 10.19) and guessing that < 5 > is proportional to a stored pattern... 

Equation 10.49 embodies Hinton, et.al. s Boltzman Machine learning scheme. Notice that it consists of two different parts. The first part, < SiSj >ciamped) is essentially the same as the Hebb rule used in Hopfield s net (equation 10.19), and reinforces the connections that lead from input to output. The second part, < SiSj >free> Can be likened to a Hebbian unlearning, whereby poor associations are effectively unlearned. 

Sejnowski, T.J. and Tesauro, G. 1989. The Hebb rule for synaptic plasticity algorithms and implementations. In JH Byrne and WO Berry (Eds.), Neural Models of Plasticity, pp. 94—103, New York, Academic Press. 

Hebb/Anti-Hebb If the desired output of i and the input to i are greater than the threshold C, this is the same as the Hebb rule, Eq. [28]. If the desired output of i is greater than the threshold, but the input to i is less than the... 

As an example of importance-weighting ideas, consider the situation that the actual interest is in hydration free energies of a distinct conformational states of a complex solute. Is there a good reference system to use to get comparative thermodynamic properties for all conformers There is a theoretical answer that is analogous to the Hebb training rule of neural networks [36, 37], and generalizes a procedure of [21]... 

Hebb s rule, long-term potentiation (LTP) and experimental models of... 

Initial evidence linking Hebb s coincidence detection rule to learning and memory 865... 

Genetic engineering of smart mice is a more stringent test of Hebb s rule 866... 

These 49 words have formed what now is known as Hebb s Learning Rule. Although some minor modifications have been required over the years, the essence of Hebb s rule remains unchanged a memory is produced by coincident neural activity when two connected nerve cells are active simultaneously, the strength of their synaptic connection increases this confers a basis for the persistence of memory. 

Initial evidence linking Hebb s coincidence detection rule to learning and memory. As the unique receptor in the brain with the coincidence-detection property, the NMDA receptor is an ideal candidate to gate the formation of memory at the synaptic level. Early observations demonstrated that infusion of NMDA receptor blockers into brain ventricles resulted in animals poor performance in the hidden-platform water maze. At first, this seemed to provide evidence for the role of hippocampal LTP in memory formation. Unfortunately, careful analyses revealed that poor performances in the water maze tests... 

There has been a steady development of neuronal analogs over the past 50 years. An important early model was proposed in 1943 by McCulloch and Pitts [23]. They described the neuron as a logical processing unit, and the influence of their model set the mathematical tone of what is being done today. Adaption or learning is a major focus of neural net research. The development of a learning rule that could be used for neural models was pioneered by Hebb, who proposed the famous Hebbian model for synaptic modification [24]. Since then, many alternative quantitative interpretations of synaptic modification have been developed [15-22]. 

The so-called Hebbian learning rule (to honour Canadian neuropsychologist Donald Hebb, who proposed it in 1949) specifies how much the weight of the connection between two neurons should increase or decrease as a function of their activation. The rule states that the weight should increase whether the two interconnected neurons are active simultaneously (otherwise the weight should decrease). This rule is not used too frequently nowadays because it works well provided that all the input patterns are orthogonal or uncorrelated. 

The simplest learning law of that kind is the Hebb law. We have already used this rule in the BAM network (Eq. (8.6)). According to the Hebb law, a weight is strengthened if the corresponding neurons Xi and y-j are simultaneously activated ... 

This rule is used in the middle two layers of the biassociative memory (BAM) network and also in the Hebb version of the BSB network. Cj is the learning rate and usually is set to 1. C2, the momentum, or momentum coefficient, is usually not used (i.e., C2 = 0). 

The succession of stimulus and key fits perfectly into Hebb s rule if two neural events take place together or shortly one after the other, the two events are connected, i.e. their succession is learned. 

With the help of Hebb s rule the stimuli are directly connected with their attached keys so that any search for the correct key is superfluous. By this, a number of cycles are saved ... 

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