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AI Brings Scientists Closer to Understanding Human Vision 2025

Петър Низамов Petar Nizamov by Петър Низамов Petar Nizamov
November 19, 2025
in 2025 News, 2025 News, Hungary Today | News from Hungary
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  • Our brain, on the other hand, works in two directions: not only does it shape the response of nerve cells at a given level of processing based on what has been achieved at previous levels, but it also shapes what will happen at the next level of processing,
  • The model developed by researchers at the HUN-REN Wigner Research Center for Physics mimics this two-way flow of information, creating an AI model that not only sees but also interprets, similar to the human brain.
  • Fact

The new AI-based vision system model described by researchers at the HUN-REN Wigner Research Center for Physics not only represents a step forward in brain function modeling, but also has the potential to make computer vision systems more reliable and accurate, the Hungarian institution announced on its website. Their findings were published in the journal Nature Communications.

Our brain is a network of densely connected areas with bidirectional connections, where the nature and role of opposite connections are still far from clear. When we see something, our brain processes the information on several levels: from simple forms to more complex concepts. Existing AI image recognition systems that can recognize a dog in a photo on our phone, for instance, work with unidirectional processing: information only travels “bottom-up.”

Our brain, on the other hand, works in two directions: not only does it shape the response of nerve cells at a given level of processing based on what has been achieved at previous levels, but it also shapes what will happen at the next level of processing,

they write.

This means that the brain always takes into account the environment and context: not only what we see, but also what it means (is the dog we see a friend or foe, is it approaching or moving away). The consequence of this is dramatic: the neural code is determined not only by what happened in processing before the given level of processing, but also by what happens in the next steps of processing.

The model developed by researchers at the HUN-REN Wigner Research Center for Physics mimics this two-way flow of information, creating an AI model that not only sees but also interprets, similar to the human brain.

With its help, not only can we explore the information processing processes of the nervous system more precisely (including such interesting phenomena as visual illusions), but we can also create more reliable and flexible computer vision systems.

In their work, Ferenc Csikor and his colleagues highlighted that the nervous system performs a more complex task than the image recognition algorithm in our phones. The nervous system needs to respond flexibly to a variety of challenges, not only identifying the type of animal being observed, but also determining whether the animal is friend or foe, moving toward us or away from us.

Traditional deep discriminative models are not suitable for adapting flexibly to different needs; instead, deep generative models must be used. According to the HUN-REN researchers, these new AI models may be more resistant to errors or attacks in the future, learn from less labeled training data, and enable much more accurate computer vision systems.

Fact

Deep discriminative models belong to deep learning systems, which are able to distinguish between different categories of inputs (e.g., images) as effectively as possible under the influence of external teaching stimuli and efficiently recognize similarities between inputs of the same category (e.g., facial recognition on the phone).

Deep generative models differ from discriminative models in that they do not require external training stimuli for learning; instead, these AI tools teach themselves. Large language models and image generation models (ChatGPT, Dall-E, Midjourney) both belong to the generative model category.

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The newly developed procedure can accelerate gene therapy progress and enable earlier treatment of serious diseases.Continue reading

Via hun-ren.hu, Featured image: Pixabay

The post AI Brings Scientists Closer to Understanding Human Vision appeared first on Hungary Today.

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