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Credit assignment in the brain
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.
Science, this issue p. 10.1126/science.aab4113