.png){kind=icon}
返回
Temporal spiking generative adversarial networks for heading direction decoding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2024.106975.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The spike-based neuronal responses within the ventral intraparietal area (VIP) exhibit intricate spatial and temporal dynamics in the posterior parietal cortex, presenting decoding challenges such as limited data availability at the biological population level. The practical difficulty in collecting VIP neuronal response data hinders the application of sophisticated decoding models. To address this challenge, we propose a unified spike-based decoding framework leveraging spiking neural networks (SNNs) for both generative and decoding purposes, for their energy efficiency and suitability for neural decoding tasks. We propose the Temporal Spiking Generative Adversarial Networks (T-SGAN), a model based on a spiking transformer, to generate synthetic time-series data reflecting the neuronal response of VIP neurons. T-SGAN incorporates temporal segmentation to reduce the temporal dimension length, while spatial self-attention facilitates the extraction of associated information among VIP neurons. This is followed by recurrent SNNs decoder equipped with an attention mechanism, designed to capture the intricate spatial and temporal dynamics for heading direction decoding. Experimental evaluations conducted on biological datasets from monkeys showcase the effectiveness of the proposed framework. Results indicate that T-SGAN successfully generates realistic synthetic data, leading to a significant improvement of up to 1.75% in decoding accuracy for recurrent SNNs. Furthermore, the SNN-based decoding framework capitalizes on the low power consumption advantages, offering substantial benefits for neuronal response decoding applications.
Keyword:
Spiking neural networks
Spiking generative adversarial networks
Heading direction decoding
期刊
IF:
6.3
论文数:
7.8K
被引数:
3.0W
机构
引用论文
Inflexibility of mental planning: A characteristic disorder with prefrontal lobe lesions?心理计划的僵化: 前额叶病变的特征性障碍？
PREAI
分享收藏Data augmentation for enhancing EEG-based emotion recognition with deep generative models使用深度生成模型增强基于EEG的情感识别的数据增强
OAAI
OAAI分享收藏Contributions of evacuation proctography and anorectal manometry to evaluation of adults with constipation and defecatory difficulty排空直肠造影和肛门直肠测压对成人便秘和排便困难的评估的贡献
PREAI
分享收藏A Multifaceted benchmarking of synthetic electronic health record generation models.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movementsSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
查看更多内容