To understand how the brain recognizes speech, appreciates music and performs other higher-level functions, it is necessary to understand how neural systems process temporal information.

Recently, scientists at Beijing Normal University studied a simple but powerful network model by which a neural system can extract long-period (several seconds in duration) external rhythms from visual input.

Moreover, the study’s findings suggest that a large neural network with a scale-free topology – that is, a network in which the probability distribution of the number of connections between its nodes follows a power law – is analogous to a repertoire where neural loops and chains form the mechanism by which exogenous rhythms are learned. Importantly, their model suggests that the brain does not necessarily require an internal clock to acquire and memorize these rhythms.

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