The Association’s oldest award, the AAAS Newcomb Cleveland Prize, supported by The Fodor Family Trust, was established in 1923 with funds donated by Newcomb Cleveland of New York City and was originally called the AAAS Thousand Dollar Prize. It is now known as the AAAS Newcomb Cleveland Prize, and its value is US$25,000. In addition to the prize funds, the winner receives a commemorative plaque, complimentary registration, and reimbursement for reasonable travel and hotel expenses to attend the AAAS Annual Meeting in order to accept the prize at the Awards Ceremony.
The prize is awarded to the author or authors of an outstanding paper published in the Research Articles or Reports sections of Science. Each annual contest starts with the first issue of June and ends with the last issue of the following May.
An eligible paper is one that includes original research data, theory, or synthesis; is a fundamental contribution to basic knowledge or is a technical achievement of far-reaching consequence; and is a first-time publication of the author’s own work. Reference to pertinent earlier work by the author may be included to give perspective.
Throughout the year, readers of Science are invited to nominate papers appearing in the Research Articles or Reports sections. Nominations must be submitted in our online form by June 30.
Please note: self-nominations will not be accepted for the AAAS Newcomb Cleveland Prize. Final selection is determined by a panel of distinguished scientists appointed by the editor-in-chief of Science.
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 Mar 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. Gütig 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.
Read a list of past recipients.
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