# DecNefSimulator: A Modular, Interpretable Framework for Decoded Neurofeedback Simulation Using Generative Models

arXiv: https://arxiv.org/abs/2511.14555 · HTML: https://mlsanity.com/paper/2511.14555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 73 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
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- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Adversarially Learned Inference](https://mlsanity.com/paper/1606.00704/) (2016)
- [Natural scene reconstruction from fMRI signals using generative latent diffusion](https://mlsanity.com/paper/2303.05334/) (2023)
- [Hierarchical VAEs Know What They Don't Know](https://mlsanity.com/paper/2102.08248/) (2021)
- [Exploration versus exploitation in reinforcement learning: a stochastic control approach](https://mlsanity.com/paper/1812.01552/) (2018)
- [MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI](https://mlsanity.com/paper/2503.02351/) (2025)
- [SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning](https://mlsanity.com/paper/2508.10298/) (2025)
- [Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines](https://mlsanity.com/paper/2410.13563/) (2024)
- [Analysis of Dominant Classes in Universal Adversarial Perturbations](https://mlsanity.com/paper/2012.14352/) (2020)
- [Generative embeddings of brain collective dynamics using variational autoencoders](https://mlsanity.com/paper/2007.01378/) (2020)
- [Active Inference Tree Search in Large POMDPs](https://mlsanity.com/paper/2103.13860/) (2021)
- [Visual Image Reconstruction from Brain Activity via Latent Representation](https://mlsanity.com/paper/2505.08429/) (2025)
- [Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery](https://mlsanity.com/paper/2408.01163/) (2024)
- [An Active Inference perspective on Neurofeedback Training](https://mlsanity.com/paper/2505.03308/) (2025)

## Abstract (arXiv)

Decoded Neurofeedback (DecNef) is a promising non-invasive approach to brain modulation with wide-ranging applications in neuromedicine and cognitive neuroscience. However, progress in DecNef research remains constrained by subject-dependent learning variability, reliance on indirect measures to quantify progress, and the high cost and time demands of experimentation. We present DecNefSimulator, a modular and interpretable simulation framework that formalizes DecNef as a machine learning problem. Beyond providing a virtual laboratory, DecNefSimulator enables researchers to model, analyze and understand neurofeedback dynamics. Using latent variable generative models as simulated participants, DecNefSimulator allows direct observation of internal cognitive states and systematic evaluation of how different protocol designs and subject characteristics influence learning. We demonstrate how this approach can (i) reproduce empirical phenomena of DecNef learning, (ii) identify conditions under which DecNef feedback fails to induce learning, and (iii) guide the design of more robust and reliable DecNef protocols in silico before human implementation. In summary, DecNefSimulator bridges computational modeling and cognitive neuroscience, offering a principled foundation for methodological innovation, robust protocol design, and ultimately, a deeper understanding of DecNef-based brain modulation.