Neural Latent Aligner: Cross-trial Alignment for Learning
Representations of Complex, Naturalistic Neural Data
- URL: http://arxiv.org/abs/2308.06443v1
- Date: Sat, 12 Aug 2023 02:35:24 GMT
- Title: Neural Latent Aligner: Cross-trial Alignment for Learning
Representations of Complex, Naturalistic Neural Data
- Authors: Cheol Jun Cho, Edward F. Chang, and Gopala K. Anumanchipalli
- Abstract summary: We propose a novel unsupervised learning framework, Neural Latent Aligner (NLA), to find well-constrained, behaviorally relevant neural representations of complex behaviors.
The proposed framework learns more cross-trial consistent representations than the baselines, and when visualized, the manifold reveals shared neural trajectories across trials.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding the neural implementation of complex human behaviors is one of
the major goals in 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 behaviors 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 (TWM) to resolve the temporal
misalignment of trials. When applied to intracranial electrocorticography
(ECoG) of natural speaking, our model learns better representations for
decoding behaviors than the baseline models, especially in lower dimensional
space. The TWM 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.
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