CASIA OpenIR  > 脑图谱与类脑智能实验室  > 神经计算与脑机交互
Simultaneous neural spike encoding and decoding based on cross-modal dual deep generative model
Qiongyi Zhou1,2; Changde Du1,2,3; Dan Li1,2; Haibao Wang1,2; Jian K. Liu5; Huiguang He1,2,4
2020
Conference NameInternational Joint Conference on Neural Networks
Conference Date2020/7/19
Conference PlaceGlasgow, United Kingdom
Abstract

Neural encoding and decoding of retinal ganglion cells (RGCs) have been attached great importance in the research work of brain-machine interfaces. Much effort has been invested to mimic RGC and get insight into RGC signals to reconstruct stimuli. However, there remain two challenges. On the one hand, complex nonlinear processes in retinal neural circuits hinder encoding models from enhancing their ability to fit the natural stimuli and modelling RGCs accurately. On the other hand, current research of the decoding process is separate from that of the encoding process, in which the liaison of mutual promotion between them is neglected. In order to alleviate the above problems, we propose a cross-modal dual deep generative model (CDDG) in this paper. CDDG treats the RGC spike signals and the stimuli as two modalities, which learns a shared latent representation for the concatenated modality and two modal-specific latent representations. Then, it imposes distribution consistency restriction on different latent space, cross-consistency and cycle-consistency constraints on the generated variables. Thus, our model ensures cross-modal generation from RGC spike signals to stimuli and vice versa. In our framework, the generation from stimuli to RGC spike signals is equivalent to neural encoding while the inverse process is equivalent to neural decoding. Hence, the proposed method integrates neural encoding and decoding and exploits the reciprocity between them. The experimental results demonstrate that our proposed method can achieve excellent encoding and decoding performance compared with the state-of-the-art methods on three salamander RGC spike datasets with natural stimuli.

Indexed ByEI
Language英语
IS Representative Paper
Sub direction classification脑机接口
planning direction of the national heavy laboratory认知机理与类脑学习
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/51622
Collection脑图谱与类脑智能实验室_神经计算与脑机交互
Corresponding AuthorHaibao Wang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
3.Huawei Cloud BU EI Innovation Lab
4.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
5.University of Leicester
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Qiongyi Zhou,Changde Du,Dan Li,et al. Simultaneous neural spike encoding and decoding based on cross-modal dual deep generative model[C],2020.
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