The recent achievements claimed that the fly visual neural system could serve a type of artificial computation model for revealing the properties of fly’s learning, memory and decision-making. It, however, still keeps open how to borrow the properties to develop artificial visual neural network optimization models. Hereby, inspired by gradient descent, fly’s visual information-processing and innate immunity mechanisms, this work probes into a fly immune visual recurrent neural network to solve large-scale global optimization (LSGO). As a two-step recurrent network, the neural network updates the output state matrix with the same size as that in the input layer through the gradient descent approach, where the learning rate of state transition is dynamically adjusted by a forward feedback fly immune visual neural network. Also, it is integrated with the conventional convolutional neural network to optimize the ultra-high dimensional weight and threshold parameters in order to acquire a prediction model of visual scene recognition. The theoretical results indicate that the recurrent neural network can converge to an equilibrium point under certain assumptions while the computational complexity is determined by the size of state matrix and the dimension of LSGO. The comparative experiments have confirmed that the neural network is an alternative and potential optimizer for LSGO problems, and in particular the acquired prediction model is a competitive tool for visual scene recognition.

最近的成就声称，苍蝇视觉神经系统可以充当一种人工计算模型，以揭示苍蝇的学习，记忆和决策能力。然而，它仍然公开如何借用属性来开发人工视觉神经网络优化模型。因此，在梯度下降，果蝇的视觉信息处理和先天免疫机制的启发下，这项工作探索了果蝇免疫的视觉递归神经网络，以解决大规模全局优化（LSGO）。作为两步递归网络，神经网络通过梯度下降方法以与输入层相同的大小更新输出状态矩阵，其中状态转换的学习率通过前向反馈飞行免疫视觉神经来动态调整。网络。还，它与常规的卷积神经网络集成在一起，以优化超高维权重和阈值参数，从而获得视觉场景识别的预测模型。理论结果表明，在一定的假设条件下，递归神经网络可以收敛到平衡点，而计算复杂度由状态矩阵的大小和LSGO的大小确定。对比实验已经证实，神经网络是LSGO问题的替代方案和潜在的优化器，特别是所获得的预测模型是视觉场景识别的竞争工具。理论结果表明，在一定的假设条件下，递归神经网络可以收敛到平衡点，而计算复杂度由状态矩阵的大小和LSGO的大小确定。对比实验已经证实，神经网络是LSGO问题的替代方案和潜在的优化器，特别是所获得的预测模型是视觉场景识别的竞争工具。理论结果表明，在一定的假设条件下，递归神经网络可以收敛到平衡点，而计算复杂度由状态矩阵的大小和LSGO的大小确定。对比实验已经证实，神经网络是LSGO问题的替代方案和潜在的优化器，特别是所获得的预测模型是视觉场景识别的竞争工具。