A novel approach to the mysteries of biological learning

A novel approach to the mysteries of biological learning

Rather than investing into more powerful muscles, better sensors, or body plans that could take them into the air, our ancestors grew big expensive brains. To be useful at all, each of these brains has to configure itself at the beginning of (and throughout) its life. Indeed, we and our phylogenetic relatives are bound to invest enormous resources to allow our offspring to configure its brains, before they can survive without us. Our research vision rests upon our conviction that the success story of big brains (which did, eventually, take us to the skies and the moon) is tightly linked to the co-evolution of learning algorithms that allow these incredibly universal and flexible machines to find the right set of parameters within the vast space of possible settings that control their function and behavior. We believe that these learning algorithms are so fundamental to the evolutionary viability of the mammalian big brain project that they are build in deeply into its fabric and connect across all of its levels from synapses to neurons, from neurons to systems, from systems to cognition, and from cognition to behavior. Given the severity of the challenge, we cannot imagine that our ancestors would not have evolved a dedicated set of behaviors that is specifically directed at configuring our brains and facilitate learning. We believe that play encompases one of these behavioral repertoires, perhaps the most crucial one. We think that current endeavours to understand biological learning are stalled because they approach learning from the perspective of supervised machine learning, i.e. the formation of externally controlled stimulus-response contingencies. Instead, we suggest that, if allowed, mammalian brains generate unique patterns of behavior associated with specific brain states that serve, instruct, and improve the learning performance of the brain. We propose to study learning in the context of these specific behaviors. In this research proposal we outline our mission to uncover the learning algorithms that subserve biological intelligence in the context of the learning behavior of play.
​

New teaching signals and self-supervised learning

How can learning in neural systems bridge between the fast time scales of neural information processing and the slow time scales of behavior? This fundamental “temporal credit assignment problem” (Hull, 1943; Sutton, 1988; Izhikevich, 2007), has long placed severe limitations on the performance of learning in neural network models and their biological validity. Recently, one of our PIs (Robert Gütig) has discovered a candidate solution of this problem with dramatic consequences for biological learning. This solution is based on three breakthroughs, the spike-threshold- surface, aggregate-labels, and self-supervised learning (Gütig, 2016) that promise to reorient the research fields of synaptic plasticity and teaching signals and herald the new field of self-supervised learning.