Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data

Abstract

It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods. However, a new challenge of neural network architecture design has also attracted attention: due to the high dimension of fMRI volume images, the manual process of network model design is very time-consuming and error prone. To tackle this problem, we proposed a Particle Swarm Optimization (PSO) based neural architecture search (NAS) framework for a deep belief network (DBN) that models volumetric fMRI data, named NAS-DBN. The core idea is that the particle swarm in our NAS framework can temporally evolve and finally converge to a feasible optimal solution. Experimental results showed that the proposed NAS-DBN framework can find robust architecture with minimal testing loss. Furthermore, we compared functional brain networks derived by NAS-DBN with general linear model (GLM), and the results demonstrated that the NAS-DBN is effective in modeling volumetric fMRI data.