Neuroscience is a field most obviously associated with medicine and/or psychology. However, my background in physics and computer science enables me to explore, and further understand, how the brain computes and stores information, identifying the underlying physical mechanisms and the interplay between them.
My longstanding work with Professor Friedemann Pulvermüller from the Freie Universität Berlin seeks to answer a number of questions, primarily how are little children able to quickly interlink signs with meanings while our closest relatives in the animal kingdom struggle to do so? In order to find answers, we work with neural network models that we compare with brain-imaging experiments.
These neural models are potential tools for improving our understanding of complex brain functions, and it is a main argument in our new Nature Reviews Neuroscience paper that they need to be neurobiologically realistic to cover the complexity of brain function; models too simple may miss relevant aspects and mechanisms.
Brain models work by simulating a large number of interconnected processing units that resemble nerve cells (neurons) and their interconnections (synapses).
Recent years have led to major advances in this area, revealing, for example, novel features of neurons, synapses, and the connectivity structure of the human neocortex, which is a large part of the central nervous system that is critical for higher level functions such as perception, language and consciousness.
Our new paper discusses different types of neural models, and what needs to happen in order for them to become as realistic, and therefore useful, as possible.
The full article Biological constraints on neural network models of cognitive function (doi: 10.1038/s41583-021-00473-5) is now available to view in Nature Reviews Neuroscience.
The paper was written in conjunction with Freie Universität Berlin, and is part of a project funded by the Engineering and Physical Sciences Research Council.
