Cerebral Cortex Advance Access originally published online on October 8, 2008
Cerebral Cortex 2009 19(6):1372-1393; doi:10.1093/cercor/bhn177
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Decoding the Cortical Transformations for Visually Guided Reaching in 3D Space
1 Centre for Vision Research, York University, Toronto, Ontario M5P 2L3 Canada, 2 Centre for Systems Engineering and Applied Mechanics and Laboratory of Neurophysiology, Université Catholique de Louvain, 1348, Louvain-la-Neuve, Belgium, 3 Centre for Neuroscience Studies, Faculty of Arts and Science, 4 Department of Physiology, Queen's University, Kingston, Ontario, K7L 3N6, Canada, 5 Department of Psychology, York University, Toronto, Ontario, M5P 2L3, Canada, 6 Departments of Biology, 7 Departments of Kinesiology and Health Sciences, York University, Toronto, Ontario, M5P 2L3, Canada
Address correspondence to Dr J. Douglas Crawford, Centre for Vision Research, York University, 4700 Keele Street, Toronto, Ontario M5P 2L3, Canada. Email: jdc{at}yorku.ca.
To explore the possible cortical mechanisms underlying the 3-dimensional (3D) visuomotor transformation for reaching, we trained a 4-layer feed-forward artificial neural network to compute a reach vector (output) from the visual positions of both the hand and target viewed from different eye and head orientations (inputs). The emergent properties of the intermediate layers reflected several known neurophysiological findings, for example, gain field–like modulations and position-dependent shifting of receptive fields (RFs). We performed a reference frame analysis for each individual network unit, simulating standard electrophysiological experiments, that is, RF mapping (unit input), motor field mapping, and microstimulation effects (unit outputs). At the level of individual units (in both intermediate layers), the 3 different electrophysiological approaches identified different reference frames, demonstrating that these techniques reveal different neuronal properties and suggesting that a comparison across these techniques is required to understand the neural code of physiological networks. This analysis showed fixed input–output relationships within each layer and, more importantly, within each unit. These local reference frame transformation modules provide the basic elements for the global transformation; their parallel contributions are combined in a gain field–like fashion at the population level to implement both the linear and nonlinear elements of the 3D visuomotor transformation.
Key Words: eye movements • head movements • arm movements • pointing • model • neural network • visuomotor transformation