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Generalization in vision and motor control

Abstract

Learning is more than memory. It is not simply the building of a look-up table of labelled images, or a phone-directory-like list of motor acts and the corresponding sequences of muscle activation. Central to learning and intelligence is the ability to predict, that is, to generalize to new situations, beyond the memory of specific examples. The key to generalization, in turn, is the architecture of the system, more than the rules of synaptic plasticity. We propose a specific architecture for generalization for both the motor and the visual systems, and argue for a canonical microcircuit underlying visual and motor learning.

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Figure 1: Tuned units in inferotemporal cortex.
Figure 2: A model of visual learning.
Figure 3: The generalization architectures of the visual and motor systems.
Figure 4: Spinal force fields combine linearly.

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Acknowledgements

We thank S. Mussa-Ivaldi, R. Shadmeher, G. Kreiman, and M. Riesenhuber for insightful and helpful comments. This research was sponsored by grants from NIH, Office of Naval Research, DARPA and National Science Foundation. Additional support was provided by Eastman Kodak Company, Daimler Chrysler, Honda Research Institute, NEC Fund, Siemens Corporate Research, Toyota, Sony and the McDermott chair (T.P.).

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Poggio, T., Bizzi, E. Generalization in vision and motor control. Nature 431, 768–774 (2004). https://doi.org/10.1038/nature03014

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