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BrainPlay - The self-teaching brain

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.

Why play?  The mysterious gain of brain function from play

Loss of function analyses of play behavior have failed. For decades biologist have tried to determine the function of play by so called play deprivation experiments. However, the effects of such play deprivation are in many instances very subtle. The most obvious behavioral deficit from play-deprivation was a loss in the ability to play. A turning point in the analysis of play effects came, when one of our PIs (Daphne Bavelier) started focusing on gains of brain function associated with intense playing. This research approach, firmly grounded in quantitative psychophysics, revealed benefits of intense playing on a wide variety of cognitive functions. Thus, within a few years the question about the function of play changed from “what is it good for?” to “how can play possibly be so beneficial for the brain?” This is the question we try to answer.

A neglected problem in neuroscience and computation

Play has been of interest to many psychologists and biologists. Such interest should not blind one for the fact that neuroscience has so far failed to uncover the neural mechanisms of playful learning. We do not know what is happening in the brains of playing animals. We have collected data about neural activity in visual cortex in tens of thousands of studies, but not a single one describes visual cortical activity during play. Numerous studies have investigated learning in operant conditioning tasks, but there is a big dearth of data, when it comes to neural data referring to playful learning. The same holds for computational analyses of learning. The recent success of brute force deep learning approaches, that rely on our most powerful computer technologies and gigantic labeled data sets, obviously bears little resemblance to the swift and easily transferable learning occurring in playing brains.

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