Mathematical formulations of Hebbian learning

Robustness of Hebbian and Anti{hebbian Learning

Self{organizing neural networks with Hebbian and anti{Hebbian learning rules were found robust against variations in the parameters of neurons of the network, such as neural activities, learning rates and noisy inputs. Robustness was evaluated from the point of view of properties of soft competition for input correlations. Two models were studied: a neural network with presynaptic Hebbian learn...

Beyond Hebbian plasticity: Effective learning with ineffective Hebbian learning rules

In this paper we revisit the classical neuroscience paradigm of Hebbian learning. We find that a necessary requirement for effective associative memory learning is that the efficacies of the incoming synapses should be uncorrelated. This is difficult to achieve in a robust manner by Hebbian synaptic learning, since it depends on network level information. Effective learning can yet be achieved ...

Associative Learning: Hebbian Flies

Fruit flies can learn to associate an odor with an aversive stimulus, such as a shock. New findings indicate that disrupting the expression of N-methyl-D-aspartate (NMDA) receptors in flies impairs olfactory conditioning. The findings provide support for a critical role for NMDA receptors in associative learning.

Differential Hebbian Learning

The differential Hebbian law ~.. = C.. 6. is examined as an 13 1 j alternative to the traditional Hebbian law ~.. = C.C. for 13 1 J updating edge connection strengths in neural networks. The motivation is that concurrent change, rather than just concurrent activation, more accurately captures the "concomitant variation" that is central to inductively inferred functional relationships. The resul...

Hebbian Learning and Plasticity