Neural Latent Aligner: Cross-trial Alignment for Learning
  Representations of Complex, Naturalistic Neural Data
Cheol Jun Cho Edward F. Chang Gopala K. Anumanchipalli
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.