Stimulus processing and unsupervised learning in autonomously active recurrent networks

Strongly recurrent neural nets may show a continuously ongoing self-sustained activity, as it is the case for the brain. A new paradigm for learning is needed for neural nets being such autonomously active, since standard Hebbian-style online learning would result in uncontrolled reinforcement of accidental activity patterns. Here we propose that autonomously active neural networks processing a time series of stimuli adapt whenever a stimulus successfully influences the ongoing internal dynamics. In this case the incoming stimulus corresponds to a novel signal. We then show, that the network performance results in an unsupervised non-linear independent component analysis of the input data stream. We propose this paradigm to be of relevance for stimulus processing in both natural and artificial neural nets.