mlsanityData to 7 Oct 2026

Reconstructing the Hemodynamic Response Function via a Bimodal Transformer

arXiv 2306.15971 ↗First posted 28 Jun 2023q-bio.NC cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

The relationship between blood flow and neuronal activity is widely recognized, with blood flow frequently serving as a surrogate for neuronal activity in fMRI studies. At the microscopic level, neuronal activity has been shown to influence blood flow in nearby blood vessels. This study introduces the first predictive model that addresses this issue directly at the explicit neuronal population level. Using in vivo recordings in awake mice, we employ a novel spatiotemporal bimodal transformer architecture to infer current blood flow based on both historical blood flow and ongoing spontaneous neuronal activity. Our findings indicate that incorporating neuronal activity significantly enhances the model's ability to predict blood flow values. Through analysis of the model's behavior, we propose hypotheses regarding the largely unexplored nature of the hemodynamic response to neuronal activity.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 28 Jun 2023.

Builds on

16 of 31 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2020End-to-End Object Detection with Transformerscited by 7,384
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  6. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  7. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  8. 2020GLU Variants Improve Transformercited by 1,724
  9. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
  10. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
  11. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,291
  12. 2020Oscar: Object-Semantics Aligned Pre-training for Vision-Language Taskscited by 1,213
4 more
  1. 2020On Layer Normalization in the Transformer Architecturecited by 906
  2. 2020End-to-End Video Instance Segmentation with Transformerscited by 443
  3. 2020MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformerscited by 355
  4. 2021Local Memory Attention for Fast Video Semantic Segmentationcited by 25

Built on by

Not cited yet.