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首页> 外文期刊>Journal of Computational Neuroscience >Functional Organization Within A Neural Network Trained To Update Target Representations Across 3-d Saccades
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Functional Organization Within A Neural Network Trained To Update Target Representations Across 3-d Saccades

机译：神经网络中的功能组织经过训练可更新3D扫视的目标表示

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摘要

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.
机译：这项研究的目的是了解神经网络如何解决双扫视任务中更新的3D方面，其中受试者对两个目标的记忆位置进行连续扫视。我们使用反向传播训练了一个3层前馈神经网络，以计算第二次扫视的3-D电机误差。网络输入是在视网膜坐标中第二个目标的方向的二维地形图，在头部固定坐标中是第一个扫视的初始眼睛方向和运动误差的3-D矢量表示。网络学会了解释更新的所有3D方面。隐层单位（HLU）显示视网膜坐标视觉感受野，并在第一个扫视中重新映射。训练产生了两类HLU，一类主要实现使用矢量减法进行更新的线性方面，第二类实现依赖于眼向的非线性更新方面。这些机制通过类似增益场的输入求和，以及通过并行优化“优化” HLU对输出的“交互作用”，在单元级别进行交互，从而将总体总体输出矢量转移到正确的第二扫视运动误差。这些观察结果可能为生物学实施更新提供线索。

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