Post and pre-compensatory Hebbian learning for categorisation

Compensatory Hebbian learning for categorisation in simulated biological neural nets

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: Abstract Using a reasonably accurate fatiguing leaky integrate and fire (FLIF) neural model, and biologically plausible comp...

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...

Hebbian Learning and Plasticity

Hebbian learning and development.

Hebbian learning is a biologically plausible and ecologically valid learning mechanism. In Hebbian learning, 'units that fire together, wire together'. Such learning may occur at the neural level in terms of long-term potentiation (LTP) and long-term depression (LTD). Many features of Hebbian learning are relevant to developmental theorizing, including its self-organizing nature and its ability...

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 ...