Map formation in proprioceptive cortex.
Current understanding of feature maps in proprioceptive cortex is quite limited. To complement experimental studies, we developed a computational model of map formation in proprioceptive cortex. Muscle length and tension from six muscle groups controlling the position of a model arm in three-dimensional space served as input to the simulated cortex. The resultant feature map consisted of regularly spaced clusters of cortical columns representing individual muscle lengths and tensions. Cortical units became tuned to plausible combinations of tension and length, and multiple representations of each muscle group were present. The map was organized such that compact regions within which all muscle group lengths and tensions are represented could be identified. Most striking was the observation that, although not explicitly present in the input, the cortical map developed a representation of the three-dimensional space in which the arm moved. These findings represent testable predictions about proprioceptive cortex, and may also help clarify some organizational issues concerning primary motor cortex.
Cho
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S
Department of Computer Science and Engineering, Pohang University of Science and Technology, Kyungbook, Korea.
Reggia
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JA
D001132:Arm / Q000502:physiology; D004550:Elbow / Q000294:innervation / Q000502:physiology; D006225:Hand / Q000294:innervation / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology; D009044:Motor Cortex / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology; D009132:Muscles / Q000294:innervation; D016571:Neural Networks, Computer; D011434:Proprioception / Q000502:physiology*