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要旨

Learning has been proposed to contribute to the recognition of biological movements. We have investigated the neural correlates of such learning processes using event-related fMRI adaptation. This paradigm entails repeated presentation of a stimulus resulting in a decrease of the fMRI resonse, compared to stronger responses after a change in a stimulus dimension. This stronger response indicates sensitivity of the measured neural populations to this changed dimension. Novel biological movements were generated by linear combination of triples of prototypical trajectories of human movements. Subjects had to discriminate between identical, very similar, moderately similar and completely different point-light stimulus pairs. The difficulty of the discrimination task could be precisely controlled by choosing appropriate weight
vectors of the prototypes in the linear combinations. Subjects were able to learn the discrimination between these novel biological motion stimuli. The fMRI results indicate that several visual areas are involved in this learning process. More specifically, lower-level motion-related areas (hMT+/V5 and KO/V3B) show an emerging sensitivity for the differences between the discriminated stimuli, and higher-level areas (STS and FFA) show an increase of sensitivity after training. In addition, we find an overall reduction of the BOLD activity after training. Our present work focuses on modeling these BOLD signal changes during discrimination learning using a hierarchical physiologically-inspired neural model for biological motion recognition [1]. We show that learning of novel templates for complex movement patterns, encoded by
sequences of body shapes and optic flow patterns, can be implemented by hebbian learning. Our model combines competitive and time-dependent hebbian plasticity in order to establish new spatio-temporal templates exploiting physiologically plausible local learning rules. Our
results demonstrate that these mechanisms can account for the emerging sensitivity for novel movement patterns observed in fMRI. We conclude that our model provides a first step to formulate and test quantitative hypotheses about the neuronal plasticity mechanisms that underlie
the learning of complex biological and non-biological movement patterns.