用深度模型从单侧大脑皮层解码双侧复杂前肢运动。
Application of an attention-based CNN-BiLSTM framework for in vivo two-photon calcium imaging of neuronal ensembles: decoding complex bilateral forelimb movements from unilateral M1
- 结合注意力机制的CNN-BiLSTM模型捕捉神经活动时空特征。
- 单侧运动皮层信号可准确解码双侧前肢复杂动作。
- 适合脑机接口与神经机制研究者阅读。
从多尺度脑网络中解码行为（如运动）仍是神经科学的核心目标。近年来，人工智能与机器学习在揭示运动功能神经机制方面作用日益显著。脑监测技术的进步使得高时空分辨率捕获复杂神经信号成为可能，这要求发展更复杂的机器学习模型以实现行为解码。本研究采用一种基于注意力机制的混合深度学习框架——CNN-BiLSTM模型，利用在体双光子钙成像获取的神经信号，解码熟练且复杂的前肢运动。结果表明，来自单侧初级运动皮层（M1）神经元群的信号能够准确解码对侧及同侧前肢的复杂运动。这些发现凸显了先进混合深度学习模型在捕捉与复杂运动执行相关的神经网络时空依赖性方面的有效性。
原文摘要 · Abstract (English)
Decoding behavior, such as movement, from multiscale brain networks remains a central objective in neuroscience. Over the past decades, artificial intelligence and machine learning have played an increasingly significant role in elucidating the neural mechanisms underlying motor function. The advancement of brain-monitoring technologies, capable of capturing complex neuronal signals with high spatial and temporal resolution, necessitates the development and application of more sophisticated machine learning models for behavioral decoding. In this study, we employ a hybrid deep learning framework, an attention-based CNN-BiLSTM model, to decode skilled and complex forelimb movements using signals obtained from in vivo two-photon calcium imaging. Our findings demonstrate that the intricate movements of both ipsilateral and contralateral forelimbs can be accurately decoded from unilateral M1 neuronal ensembles. These results highlight the efficacy of advanced hybrid deep learning models in capturing the spatiotemporal dependencies of neuronal networks activity linked to complex movement execution.
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