Neural network models of the future – the key to unlocking how our brain works

From localist networks to whole brain networks, there are several different models at different levels of scale and detail that can help us better understand what goes on in our head when we see, feel, or speak.

We believe that neural network models need to bridge the gaps between approximate human brain models at different levels in order to understand brain function in full. Specifically, we need to understand the microscopic level of nerve cell function (roughly at micrometer scale), the mesoscopic level of interactions in local neuron clusters (millimeters), and the macroscopic level visible to the naked eye (centimeters). We can then study the interplay between processes on these scales and approach models that simulate brain circuits across all of them. This seems to be crucial to make progress beyond studies of single, isolated aspects of brain function; various groups worldwide have started to engage with this idea.

This novel approach, known as brain-constrained modelling, uses recent neurobiological material evidence at different levels of spatial resolution to make neural networks more realistic, in order to work towards mechanistic counterparts of abilities specific to humans.

The brain constrained models are used to simulate human cognitive functions, and relate them to the neuronal material that has changed in brain evolution – for example, understanding the difference between macaque monkeys and humans. It is important that we understand whether the material structure might relate to functional cognitive changes.

For example: we know a lot about new evolutionary inventions realised in the human brain, but what we do not know is what, let’s say, a novel fiber bundle or cortical area functionally contributes to cognition. A fiber bundle on its own might appear to be a rather dumb piece of material.