Robert Gütig is a group leader at the Max Planck Institute of Experimental Biology in Goettingen where he researches spike-based learning and information processing in neural networks. He was trained in Physics at the Free University of Berlin (Germany) and the University of Cambridge (UK). He did a PhD in Computational Neuroscience with Ad Aertsen (University of Freiburg, Germany) and a postdoc with Haim Sompolinsky (Hebrew University (Israel) and Harvard University (USA)).

Abstract: The brain routinely discovers sensory clues that predict opportunities or dangers. However, it is unclear how neural learning processes can bridge the typically long delays between sensory clues and behavioral outcomes. Here, I introduce a learning concept, aggregate-label learning, that enables biologically plausible model neurons to solve this temporal credit assignment problem. Aggregate-label learning matches a neuron’s number of output spikes to a feedback signal that is proportional to the number of clues but carries no information about their timing. Aggregate-label learning outperforms stochastic reinforcement learning at identifying predictive clues and is able to solve unsegmented speech-recognition tasks. Furthermore, it allows unsupervised neural networks to discover reoccurring constellations of sensory features even when they are widely dispersed across space and time.

This work has appeared at http://science.sciencemag.org/content/351/6277/aab4113