Open AccessPoster presentation Learning through activity-dependent plasticity modulation

Hebbian learning has been implicated as a possible mech-anism in a wide range of learning and memory functions in the brain. A large body of theoretical studies and simu-lations has investigated its implications in the dynamics of single neuron as well as network models. For example, neural network models have been found to produce meaningful internal states when driven by structured external stimulation. These studies, however, typically lack a notion of a "desired output " in the form of a well specified pattern of network activity, corresponding to a relevant functional output. To impose a desired input-output relation, various forms of supervised learning (or at least reinforcement in the form of an external cue) are often invoked. Recently there has been increasing interest in computational models that involve a separation o