AAAS Newcomb Cleveland Prize
2016 Award Recipient
The 2016 Newcomb Cleveland Prize was awarded to Robert Gütig for his outstanding research article "Spiking neurons can discover predictive features by aggregate-label learning," published in Science 4 March 2016.
To discover relevant clues for survival, an organism must bridge the gap between the short time periods when a clue occurs and the potentially long waiting times after which feedback arrives. This so-called temporal credit-assignment problem is also a major challenge in machine learning. Gi.itig developed a representation of the responses of spiking neurons, whose derivative defines the direction along which a neuron's response changes most rapidly. By using a learning rule that follows this development, the temporal credit-assignment problem can be solved by training a neuron to match its number of output spikes to the number of clues. The same learning rule endows unsupervised neural networks with powerful learning capabilities. The paper has considerable significance both for neuroscience and for machine learning. For neuroscience, it presents an intriguing and testable hypothesis about communication within the brain. For machine learning, the algorithm developed is demonstrated in the paper to have considerable power.
The 2015-2016 Newcomb Cleveland Prize Selection Committee included Jeremy Berg, Chair, Editor-in-Chief, Science as well as Cori Bargmann, Howard Hughes Medical Institute, Kavli Neural Systems Institute, The Rockefeller University; Gyorgy Buzsaki, New York University School of Medicine; Jennifer Doudna, University of California, Berkeley; Harinder Singh, Cincinnati Children's Hospital Medical Center; Maria Zuber, Massachusetts Institute of Technology.
Please click here for a list of past recipients.