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

Abstract

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.