Gradient-based fly immune visual recurrent neural network solving large-scale global optimization

Abstract

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.