title: Spiking neurons can discover predictive features by aggregate-label learning.
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.
authors.affiliation: Max Planck Institute of Experimental Medicine, Hermann-Rein-Strasse 3, 37075 Göttingen, Germany. guetig@em.mpg.de.
authors.forename: Robert
authors.initials: R
authors.lastname: Gütig
mesh_terms: D001921:Brain / Q000502:physiology*; D003210:Concept Formation / Q000502:physiology*; D000068156:Formative Feedback; D006801:Humans; D007858:Learning / Q000502:physiology*; D008564:Membrane Potentials / Q000502:physiology; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D012054:Reinforcement, Psychology; D013060:Speech / Q000502:physiology; D000069553:Supervised Machine Learning; D000069558:Unsupervised Machine Learning