Thomas Nortmann

@thonor.bsky.social

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Computational neuroscience. PhD student with Friedemann Zenke at FMI, Basel. B.Sc. and M.Sc. Cognitive Science at university Osnabrück

A screenshot of the article "Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision". The authors are Thomas Nortmann, Philip Sulewski and Tim C. Kietzmann. Under the article heading is a section titled "The bigger picture". The section reads as follows: "We show how some of the brain’s amazing abilities, such as keeping our view of the world stable even when our eyes move, might come from simple ideas such as saving energy. Instead of assuming that the brain is wired with complex instructions for predicting what we will see next, we explored whether these skills could develop naturally from basic physical principles. We apply a computer model that mimics how our eyes move and how the brain processes visual information. This model was trained to perform eye movements while trying to use as little energy as possible by reducing unnecessary neural activity. Surprisingly, as the model learned to be more energy efficient, it started to develop a process called predictive remapping. This process is how the brain predicts what will appear in our vision after an eye movement, so our perception stays smooth and stable. Moreover, the model learned to create an internal representation that translates the position of the eyes into a more stable, environment-centered frame of reference. This internal map helps the system predict future visual input and decide when to inhibit or reduce certain signals, making the whole process more efficient. Altogether, we show that complex visual functions such as predictive remapping and creating an environment-centered reference frame can emerge naturally when a system is optimized for energy efficiency."