mlsanityData to 7 Oct 2026

Large-scale neural recordings call for new insights to link brain and behavior

arXiv 2103.14662 ↗First posted 26 Mar 2021q-bio.NCcited by 28

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Abstract (arXiv)

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. Here, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modelling frameworks to interpret these data, and argue that interpreting brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding at stake. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 7; 2024: 6; 2025: 7; 2026: 3.

28 papers.

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  1. 2022Learnable latent embeddings for joint behavioral and neural analysiscited by 113
  2. 2023A Unified, Scalable Framework for Neural Population Decodingcited by 59
  3. 2024Towards a "universal translator" for neural dynamics at single-cell, single-spike resolutioncited by 27
  4. 2024BrainWave: A Brain Signal Foundation Model for Clinical Applicationscited by 26
  5. 2022Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformerscited by 18
  6. 2024NeuroBind: Towards Unified Multimodal Representations for Neural Signalscited by 13
  7. 2024Neural population geometry and optimal coding of tasks with shared latent structurecited by 9
  8. 2025Generalizable, real-time neural decoding with hybrid state-space modelscited by 8
  9. 2021Can Information Flows Suggest Targets for Interventions in Neural Circuits?cited by 6
  10. 2023Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Lossescited by 6
  11. 2023Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learningcited by 6
  12. 2023Latent Processes Identification From Multi-View Time Seriescited by 5
16 more
  1. 2023Data efficiency, dimensionality reduction, and the generalized symmetric information bottleneckcited by 5
  2. 2024Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systemscited by 5
  3. 2025Forecasting Whole-Brain Neuronal Activity from Volumetric Videocited by 5
  4. 2025Animal behavioral analysis and neural encoding with transformer-based self-supervised pretrainingcited by 5
  5. 2024Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equationscited by 4
  6. 2025Equivariance by Contrast: Identifiable Equivariant Embeddings from Unlabeled Finite Group Actionscited by 2
  7. 2025A ghost mechanism: An analytical model of abrupt learning in recurrent networkscited by 1
  8. 2026BrainPilot: Automating Brain Discovery with Agentic Researchcited by 1
  9. 2021Overcoming the Domain Gap in Neural Action Representationscited by 0
  10. 2022Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populationscited by 0
  11. 2023Mapping EEG Signals to Visual Stimuli: A Deep Learning Approach to Match vs. Mismatch Classificationcited by 0
  12. 2023Neural Crystalscited by 0
  13. 2025NLP4Neuro: Sequence-to-sequence learning for neural population decodingcited by 0
  14. 2025Learning Mixtures of Linear Dynamical Systems via Hybrid Tensor-EM Methodcited by 0
  15. 2026Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behaviorcited by 0
  16. 2026Machine Learning Methods for Studying Latent Neural Activity Dynamicscited by 0