Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data
Jul 24, 2023
Speakers
Cheol Jun Cho
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Edward Chang
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Gopala A. Anumanchipalli
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About
Understanding the neural implementation of complex human behaviors is one of the major goals of neuroscience. To this end, it is crucial to find a true representation of the neural data, which is challenging due to the high complexity of the task and the low signal-to-ratio (SNR) of the signals. Here, we propose a novel unsupervised learning framework, Neural Latent Aligner (NLA), to find well-constrained, behaviorally relevant neural representations of complex behaviors. The key idea is to align representations across repeated trials to learn cross-trial consistent information. Furthermore, we propose a novel, fully differentiable time-warping model to resolve the temporal misalignment of the trials. When applied to intracranial electrocorticography (ECoG) of natural speaking, our model learns better representations for decoding behaviors, especially in lower dimensional space, than the baseline models. The time-warping model is empirically validated by measuring behavioral coherence between aligned trials. The proposed framework learns more cross-trial consistent representations than the baselines, and when visualized, the manifold reveals shared neural trajectories across trials.Understanding the neural implementation of complex human behaviors is one of the major goals of neuroscience. To this end, it is crucial to find a true representation of the neural data, which is challenging due to the high complexity of the task and the low signal-to-ratio (SNR) of the signals. Here, we propose a novel unsupervised learning framework, Neural Latent Aligner (NLA), to find well-constrained, behaviorally relevant neural representations of complex behaviors. The key idea is to alig…
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