< Back to news
The Shallow Brain hypothesis
Not only AI systems, but also theories of brain function often assume deep networks. The theory of Predictive Coding (predictive coding) provides an important framework for understanding brain functions, which posits that the brain constantly generates and updates internal models of the environment. Predictive Coding models also predominantly assume deep, hierarchically organised networks.
In a recent paper published in Nature Reviews Neuroscience, researchers from the University of Amsterdam and the University of Tartu (Estonia) propose a groundbreaking new theory - the Shallow Brain hypothesis - that challenges the commonly held view that neural computation occurs exclusively through hierarchical structures.
This article was published on the website of the University of Amsterdam (in Dutch).
The image was generated by the University of Amsterdam using Adobe Firefly (keywords: shallow brain architecture).
14 November 2023
A new theory sheds light on the ‘shallow’ structure of the brain and AI
Recent advances in artificial intelligence are astounding. Some people even claim that AI systems are already sentient.
These AI systems often use so-called 'deep learning' networks where information is processed through an accumulation of interconnected layers (therefore called 'deep'), each consisting of artificial, mathematically defined neurons. It is believed that deeper networks (i.e. with more and more layers of artificial neurons) have more computational power; therefore, the current trend in AI is to use such deep network architectures.
The Shallow Brain hypothesis
Not only AI systems, but also theories of brain function often assume deep networks. The theory of Predictive Coding (predictive coding) provides an important framework for understanding brain functions, which posits that the brain constantly generates and updates internal models of the environment. Predictive Coding models also predominantly assume deep, hierarchically organised networks.
In a recent paper published in Nature Reviews Neuroscience, researchers from the University of Amsterdam and the University of Tartu (Estonia) propose a groundbreaking new theory - the Shallow Brain hypothesis - that challenges the commonly held view that neural computation occurs exclusively through hierarchical structures.
According to this new theory, the brain is characterised by a shallow structure elegantly intertwined with the conventional, deep hierarchy of cortical regions. Shallow, fast parallel computations and deep, slow computations coexist in the brain without interfering with each other. They can even reinforce each other by offering shortcuts for decisions that would otherwise take too long. This theory inspires AI research to look for new directions and better imitate the brain.
This article was published on the website of the University of Amsterdam (in Dutch).
The image was generated by the University of Amsterdam using Adobe Firefly (keywords: shallow brain architecture).
Vergelijkbaar >
Similar news items
14 November 2024
The Amsterdam Vision on AI: A Realistic View on Artificial Intelligence
In its new policy, The Amsterdam Vision on AI , the city outlines how artificial intelligence (AI) should be integrated into urban life and how it should influence the city according to its residents. This vision was developed through months of conversations and dialogues with a wide range of Amsterdammers—from festival-goers to schoolchildren, experts to novices—who shared their thoughts on the future role of AI in Amsterdam.
read more >
14 November 2024
Interview: KPN Responsible AI Lab with Gianluigi Bardelloni and Eric Postma
ICAI's interview appeared this time with Gianluigi Bardelloni and Eric Postma, they talk about the developments in their ICAI Lab.
read more >
November 14
AI pilots TLC Science: generative AI in academic education
The University of Amsterdam has launched a new project through its Teaching & Learning Centre Science, exploring how Generative AI, like ChatGPT, can enhance academic education. This pilot program at the Faculty of Science tests and evaluates various applications of GenAI in higher education.
read more >