Reproducibility is key for scientific progress. If research results cannot be reproduced and trusted, other researchers cannot build on them.

Reproducibility is a challenge also in computational neuroscience, and today's guest has worked on how this can be remedied, for example, through standardized model description and model sharing.

He also recently organised a workshop celebrating a decade with the (reproducible) Potjans-Diesmann neural network model, which has become an important community tool.

Historically, the analysis of neural recordings focused on responses of single neurons recorded by single-contact electrodes. Modern electrodes with multiple electrode contacts can instead record spikes (action potentials) from hundreds of neurons simultaneously.

Manifold analysis of the overall population activity of these neurons has become a critical tool for interpretation of such data.

The podcast guest is a pioneer in the development and use of such analysis.

Neurons need particular sodium and potassium concentration gradients across their membranes to function. These gradients are set up by so-called ion pumps which require energy stored in ATP molecules to run.

ATP is the common energy currency in the brain and is produced from nutrients delivered by the blood by a complicated set of chemical reactions known as a metabolic network.

Today's guest has just published a comprehensive model of such a network and explains how it can shed light on differences between young and brains.

An important discovery that has come out of computational neuroscience, is that cortical neurons in vivo appear to receive so-called balanced inputs.

In the balanced state the excitatory and inhibitory synaptic inputs to a neuron are about equal, and action potentials occur when a fluctuation temporarily makes the excitation dominate.

The theory, for example, explains the observed irregular firing of cortical neurons in the background state.

Today's guest was one of the key developers of the theory in the late 1990s.

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?