How can computational neuroscience contribute to developing neurotechnology to help people with brain disorders and disabilities?

This was the topic of a panel debate I hosted at the 34th Annual Computational Neuroscience Meeting in Florence in July this year.

Electric or magnetic recording and/or stimulation are key clinical tools for helping patients, and the three panelists have all used computational methods to aid this endeavor.  

In an adversarial collaboration researchers with opposing theories jointly investigate a disputed topic by designing and implementing a study in a mutually agreed unbiased way.

Results from adversarial testing of two well-known theories for consciousness, Global Neuronal Workspace Theory (GNWT) and Integrated Information Theory (IIT), were presented earlier this year.

In this podcast one of the proponents and developers of IIT describes this candidate theory, and also the design of, and results from, the adversarial study. 

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. 

A future computational neuroscience project could be to model not only the signal processing properties of neurons, but also all processes that keep a neuron alive for, say, a 100-year life span.

In 2012 the group of the guest published the first such whole-cell model for a very simple bacterium (M. genitalia). In 2020 a model of the larger E. coli bacterium comprising 10.000 equations and 19.000 model parameters was presented.

How are such models built, and what can they do?  

Numerous neuron models have been made, but most of them are "single-purpose" in that they are made to address a single scientific question. In contrast, multipurpose neuron models are made to be used to address many scientific questions.

In 2011, the guest published a multipurpose rodent pyramidal-cell model which has been actively used by the community ever since.

We talk about how such models are made, and how his group later built human neuron models to explore network dynamics in brains of depressed patients.

With modern electrical and optical measurement techniques, we can now measure neural activity in hundreds or thousands of neurons simultaneously. This allows for the investigation of population codes, that is, of how groups of neurons together encode information.

In 2019 today’s guest published a seminal paper with collaborators at UCL in London where analysis of optophysiological data from 10.000 neurons in mouse visual cortex revealed an intriguing population code balancing the needs for efficient and robust coding.

We discuss the paper and (towards the end) also how new AI tools may be a game-changer for neuroscience data analysis.

The observed variety of dendritic structures in the brains is striking. Why are they so different, and what determine the branching patterns?

Following the dictum “if you understand it, you can build it”, the lab of the guest builds dendritic structures in a computer and explore the underlying principles.

Two key principles seem to be to minimize (i) the overall length of dendrites and (ii) the path length from the synapses to the soma. 

The term “foundation model” refers to machine learning models that are trained on vast datasets and can be applied to a wide range of situations. The large language model GPT-4 is an example.

The group of the guest has recently presented a foundation model for optophysiological responses in mouse visual cortex trained on recordings from 135.000 neurons in mice watching movies.

We discuss the design, validation, use of this and future neuroscience foundation models.