A promise of basic neuroscience research is that the new insights will lead to new cures for brain diseases. But has that happened so far?

Today’s guest, an accomplished professor of neuroscience, decided to investigate. Her book “Elusive cures: why neuroscience hasn’t solved brain disorders - and how we can change that" came out this summer.

Here she argues that we need to consider the brain as a complex adaptive system, not as a chain of dominos as in the typical linear thinking.

Synaptic plasticity underlies several key brain functions including learning, information filtering and homeostatic regulation of overall neural activity.

While several mathematical rules have been developed for plasticity both at excitatory and inhibitory synapses, it has been difficult to make such rules co-exist in network models.

Recently the group of the guest has explored how co-dependent plasticity rules can remedy the situation and, for example, assure that long-term memories can be stored in excitatory synapses while inhibitory synapses assure long-term stability.

Computational neuroscientists rely on simplification when they make their models. But what is the right level of simplification?

When should we, for example, use a biophysically detailed model and when a simplified abstract model when modelling neural dynamics? What are the problems of simplifying too much, or too little?

This was the topic of the panel discussion between a science philosopher (MC), author of the recent book “The Brain Abstracted”, and an experienced modeler (TS) at the FENS Regional Meeting in Oslo in June 2025.