ReviewFrontiers in artificial intelligence2022
Generative Models of Brain Dynamics.
Review in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Associative emotional memory encoding: insights from network stability analysis of an fMRI-driven bilinear dynamics.Frontiers in systems neuroscience · 2026Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Universal differential equations as a unifying modeling language for neuroscience.Frontiers in computational neuroscience · 2025Review
- Analyzing the brain's dynamic response to targeted stimulation using generative modeling.Network neuroscience (Cambridge, Mass.) · 2025Review
- Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain.Social cognitive and affective neuroscience · 2024Review
- Recent Progress in Brain Network Models for Medical Applications: A Review.Health data science · 2024Review
- Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns.Frontiers in computational neuroscience · 2024Article
- Article
- Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.NeuroImage · 2023Article
- Multitask computation through dynamics in recurrent spiking neural networks.Scientific reports · 2023Article
- Phase relations of interneuronal activity relative to theta rhythm.Frontiers in neural circuits · 2023Article
- A deep network-based model of hippocampal memory functions under normal and Alzheimer's disease conditions.Frontiers in neural circuits · 2023Article
- Precise Spiking Motifs in Neurobiological and Neuromorphic Data.Brain sciences · 2022Review
- Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers.Advances in neural information processing systems · 2022Article
- Brain-heart interactions in the neurobiology of consciousness.Current research in neurobiology · 2022Review
- Dynamic Oscillations Evoked by Subcallosal Cingulate Deep Brain Stimulation.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.