Large flocks of birds can rapidly change their direction without it being clear how such a decision develops, and whether some birds have a larger influence on it than others. Since the behavior of any one bird depends on that of its neighbors, answering this question is rather complicated. A similar problem is faced by neuroscientists who want to find out which neurons in a large network caused a particular decision.
Photo: Christoffer A. Rasmussen, CreativeCommons CC 1.0
To approach this question, experimental researchers have so far considered the information that an individual sensory neuron carries about the final decision. Just as an individual is considered suspicious if he or she is found to have insider information about a crime, those sensory neurons whose activity contains information about the eventual decision are presumed to have played a role in reaching the final decision. The problem with this approach is that neurons - much like people – are constantly communicating with each other. A neuron which itself is not involved in the decision may simply have received this information from a neighboring neuron, and “join the conversation”. Actually, the neighboring cell sends out the crucial signal transmitted to the higher decision areas in the brain.The new formula that has been developed by scientists addresses this by accounting not just for the information in the activity of any one neuron but also for the communication that takes place between them. This formula will now be used to determine whether only a few neurons that carry a lot of information are involved in the brain's decision process, or whether the information contained in very many neurons gets combined. In particular, it will be possible to address the more fundamental question: In which decisions does the brain use information in an optimal way, and for which decisions is its processing suboptimal?
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