A team of researchers in the Netherlands has proposed a new way of designing computer models of the brain – an approach that could also influence future artificial intelligence (AI) systems.
In most deep learning architectures, information is processed step by step through tens of layers inside the cortex, the brain's main structure involved in high-level functions like perception and decision-making. However, neuroscientists know that the cortex is also closely connected with deeper brain regions, known as subcortical structures – which are involved in processes such as regulating body movement, emotion and learning stimulus-response behaviours – and these connections are overlooked by most artificial neural networks.
In a new study supported by the Human Brain Project and published in Current Research in Neurobiology, the researchers introduce a computational model that incorporates these connections, combining a hierarchical architecture typical of the cortex with faster, subcortical pathways. This proposed architecture is more parallel – having a hierarchical, cortical route and a "shallow", subcortical route – and may better reflect how the brain works.
"Our model addresses key limitations in existing deep learning and predictive coding networks, offering a more biologically plausible and functionally advantageous alternative", say the authors.
The work builds on the authors' 2023 proposal of the "Shallow Brain Hypothesis", which argues that the brain relies on both hierarchical processing in the cortex and parallel interactions with subcortical regions. The team has now developed a model combining both pathways found in the brain.
They implemented this approach using two common AI frameworks – a convolutional neural network and a hierarchical predictive coding model – and tested it on a decision-making task. Their results show that the two pathways complement each other: the fast subcortical route can guide simple stimulus-response decisions, while more complex tasks rely on the 'deep' cortical network.
Together, this parallel architecture allows for more flexible and efficient processing, suggesting that current AI models may well be missing an important principle of how the brain works.
