Learning Time-Scale Invariant Population-Level Neural Representations

• URL: http://arxiv.org/abs/2511.13022v1
• Date: Mon, 17 Nov 2025 06:20:31 GMT
• Title: Learning Time-Scale Invariant Population-Level Neural Representations
• Authors: Eshani Patel, Yisong Yue, Geeling Chau,
• Abstract summary: General-purpose foundation models for neural time series can help accelerate neuroscientific discoveries and enable applications such as brain computer interfaces (BCIs)<n>A key component in scaling these models is population-level representation learning.<n>Population-level approaches have recently shown that such representations can be both efficient to learn on top of pretrained temporal encoders and produce useful representations for decoding a variety of downstream tasks.
• Score: 24.716617214869753
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: General-purpose foundation models for neural time series can help accelerate neuroscientific discoveries and enable applications such as brain computer interfaces (BCIs). A key component in scaling these models is population-level representation learning, which leverages information across channels to capture spatial as well as temporal structure. Population-level approaches have recently shown that such representations can be both efficient to learn on top of pretrained temporal encoders and produce useful representations for decoding a variety of downstream tasks. However, these models remain sensitive to mismatches in preprocessing, particularly on time-scales, between pretraining and downstream settings. We systematically examine how time-scale mismatches affects generalization and find that existing representations lack invariance. To address this, we introduce Time-scale Augmented Pretraining (TSAP), which consistently improves robustness to different time-scales across decoding tasks and builds invariance in the representation space. These results highlight handling preprocessing diversity as a key step toward building generalizable neural foundation models.

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