Modeling of a time-varying dynamical system provides insights into the functions of biological neural networks and contributes to the development of next-generation neural prostheses. In this paper, we have formulated a novel sparse multiwavelet-based generalized Laguerre–Volterra (sMGLV) modeling framework to identify the time-varying neural dynamics from multiple spike train data. First, the significant inputs are selected by using a group least absolute shrinkage and selection operator (LASSO) method, which can capture the sparsity within the neural system. Second, the multiwavelet-based basis function expansion scheme with an efficient forward orthogonal regression (FOR) algorithm aided by mutual information is utilized to rapidly capture the time-varying characteristics from the sparse model. Quantitative simulation results demonstrate that the proposed sMGLV model in this paper outperforms the initial full model and the state-of-the-art modeling methods in tracking performance for various time-varying kernels. Analyses of experimental data show that the proposed sMGLV model can capture the timing of transient changes accurately. The proposed framework will be useful to the study of how, when, and where information transmission processes across brain regions evolve in behavior.

Reconstructing visual stimulus from human brain activity measured with functional magnetic resonance imaging (fMRI) is a challenging decoding task for revealing the visual system. Recent deep learning approaches commonly neglect the relationship between hierarchical image features and different regions of the visual cortex, and fail to use global and local image features in reconstructing visual stimulus. To address these issues, in this paper, a novel neural decoding framework is proposed by using a dual attention hierarchical latent generative network with multi-scale feature fusion (DA-HLGN-MSFF) method. Specifically, the fMRI data is firstly encoded to hierarchical features of our image encoder network which employs a multi-kernel convolution block to extract the multi-scale spatial information of images. In order to reconstruct the perceived images and further improve the performance of our generator network, a dual attention block based on channel-spatial attention mechanism is then proposed to exploit the inter-channel relationships and spatial long-range dependencies of features. Moreover, a multi-scale feature fusion block is finally adopted to aggregate the global and local information of features at different scales and synthesize the final reconstructed images in the generator network. Competitive experimental results on two public fMRI datasets demonstrate that our method is able to achieve promising reconstructing performance compared with the state-of-the-art methods. The codes of our proposed DA-HLGN-MSFF method will be open access on https://github.com/ljbuaa/HLDAGN.