A learning rule for dynamic recruitment and decorrelation

The interest in neuronal networks originates for a good part in the option not to construct, but to train them. The mechanisms governing synaptic modi fications during such training are assumed to depend on signals locally ava ilable at the synapses. In contrast, the performance of a network is suitab ly measured on a global scale. Here we propose a learning rule that address es this conflict. It is inspired by recent physiological experiments and ex ploits the interaction of inhibitory input and backpropagating action poten tials in pyramidal neurons. This mechanism makes information on the global scale available as a local signal. As a result, several desirable features can be combined: the learning rule allows fast synaptic modifications appro aching one-shot learning. Nevertheless, it leads to stable representations during ongoing learning. Furthermore, the response properties of the neuron s are not globally correlated, but cover the whole stimulus space. (C) 2000 Elsevier Science Ltd. All rights reserved.