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Next-generation neural computations
Next-generation neural computations
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Andrew Isaac Meso

Andrew Isaac Meso

Lecturer, King’s College London (IOPPN).

Main collaborative work:

  • Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2015). Biologically Inspired Dynamic Textures for Probing Motion Perception. Advances in Neural Information Processing Systems.

    Cite Pdf PDF arXiv

  • Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018). Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. Neural Computation.

    PDF Cite DOI URL arXiv

  • Andrew Isaac Meso, Jonathan Vacher, Nikos Gekas, Pascal Mamassian, Laurent U Perrinet, Guillaume S Masson (2025). DynTex: A Real-Time Generative Model of Dynamic Naturalistic Luminance Textures. Journal of Vision.

    Cite DOI URL HAL

Latest

  • DynTex: A Real-Time Generative Model of Dynamic Naturalistic Luminance Textures
  • An open-source vision-science tool for the auto-regressive generation of dynamic stochastic textures Motion Clouds
  • Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
  • A Mathematical Account of Dynamic Texture Synthesis for Probing Visual Perception
  • Biologically Inspired Dynamic Textures for Probing Motion Perception
  • Beyond simply faster and slower: exploring paradoxes in speed perception
  • Dynamic Textures For Probing Motion Perception
  • How and why do image frequency properties influence perceived speed?

© 2026 Laurent U Perrinet. This work is licensed under CC BY NC ND 4.0

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