mlsanityData to 7 Oct 2026

Exploring Biological Neuronal Correlations with Quantum Generative Models

arXiv 2409.09125 ↗First posted 13 Sep 2024quant-ph cs.LG cs.NEcited by 0

Not on the map. Why?

Abstract (arXiv)

Understanding of how biological neural networks process information is one of the biggest open scientific questions of our time. Advances in machine learning and artificial neural networks have enabled the modeling of neuronal behavior, but classical models often require a large number of parameters, complicating interpretability. Quantum computing offers an alternative approach through quantum machine learning, which can achieve efficient training with fewer parameters. In this work, we introduce a quantum generative model framework for generating synthetic data that captures the spatial and temporal correlations of biological neuronal activity. Our model demonstrates the ability to achieve reliable outcomes with fewer trainable parameters compared to classical methods. These findings highlight the potential of quantum generative models to provide new tools for modeling and understanding neuronal behavior, offering a promising avenue for future research in neuroscience.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 52 references that are in the map. First posted on arXiv 13 Sep 2024.

Builds on

16 of 52 references.

References · 16

  1. 2016Improved Techniques for Training GANscited by 2,938
  2. 2017Improved Training of Wasserstein GANscited by 2,223
  3. 2020The power of quantum neural networkscited by 377
  4. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 328
  5. 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 284
  6. 2018Quantum generative adversarial networkscited by 128
  7. 2020Experimental Quantum Generative Adversarial Networks for Image Generationcited by 83
  8. 2017Deep Learning Models of the Retinal Response to Natural Scenescited by 36
  9. 2019Quantum Wasserstein Generative Adversarial Networkscited by 33
  10. 2022STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformercited by 23
  11. 2022Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generationcited by 22
  12. 2013Searching for collective behavior in a network of real neuronscited by 9
4 more
  1. 2022Power of Quantum Generative Learningcited by 6
  2. 2021Fitting summary statistics of neural data with a differentiable spiking network simulatorcited by 5
  3. 2021Connectivity Concepts in Neuronal Network Modelingcited by 1
  4. 2022Time-dependent maximum entropy model for populations of retinal ganglion cellscited by 1

Built on by

Not cited yet.