Details

Autor(en) / Beteiligte

• Keith, Gerald P
• Smith, Michael A
• Crawford, J Douglas

Titel

Functional organization within a neural network trained to update target representations across 3-D saccades

Ist Teil von

• Journal of computational neuroscience, 2007-04, Vol.22 (2), p.191-209

Ort / Verlag

United States: Springer Nature B.V

Erscheinungsjahr

2007

Quelle

MEDLINE

Beschreibungen/Notizen

• The goal of this study was to understand how neural networks solve the 3-D aspects of updating in the double-saccade task, where subjects make sequential saccades to the remembered locations of two targets. We trained a 3-layer, feed-forward neural network, using back-propagation, to calculate the 3-D motor error the second saccade. Network inputs were a 2-D topographic map of the direction of the second target in retinal coordinates, and 3-D vector representations of initial eye orientation and motor error of the first saccade in head-fixed coordinates. The network learned to account for all 3-D aspects of updating. Hidden-layer units (HLUs) showed retinal-coordinate visual receptive fields that were remapped across the first saccade. Two classes of HLUs emerged from the training, one class primarily implementing the linear aspects of updating using vector subtraction, the second class implementing the eye-orientation-dependent, non-linear aspects of updating. These mechanisms interacted at the unit level through gain-field-like input summations, and through the parallel "tweaking" of optimally-tuned HLU contributions to the output that shifted the overall population output vector to the correct second-saccade motor error. These observations may provide clues for the biological implementation of updating.