A bursting neuron can maintain its firing-pattern identity throughout an animal's life, even though the ion-channel proteins underlying this identity are turned over on the timescale of days. Today's guest has proposed that neuronal identities are stored in the specific protein production rules, which are regulated by intracellular calcium signaling. And how can animals reliably perform a learned task for weeks, even when the underlying neural representation drifts over time, so-called representational drift?

Most neural network models till date have assumed all neurons to be identical, or at least that all neurons within a population are identical. In reality, no two neurons are completely the same.

Is this due to unavoidable "biological noise" that the nervous system has to cope with, or can it be a useful feature included by design?

The guest co-wrote the recent paper "How heterogeneity shapes dynamics and computation in the brain" addressing this question.

Fruit flies need a short-term (working) memory to keep their direction when they navigate their way to the fruit by smelling.

Mean-field ring models was theoretically suggested to encode stimulus orientations 30 years and was observed in fruit-fly compass neurons 10 years ago. But how does odor input come into the picture to set the compass course?

The group of the guest has studied the question with a host of different experimental and theoretical methods.

Starting with the work of pioneers like Wilson and Cowan in the 1970s, mean‑field models have become a dominant tool for modeling neural activity at the level of neuronal populations.

Despite their popularity, most mean‑field models have been heuristic and not systematically derived from the underlying 'microscopic' dynamics of individual neurons.

Today's guest has made important contributions towards remedying this situation.

While some models aim to explain qualitative features of brain activity, other aim to reproduce experimental data quantitatively. If so, model parameters must be adjusted to make the model predictions fit the experimental data.

A complication is that in most neurobiological applications, there is not a unique best fit: many parameter combinations give equally good model fits.

Recently, the guest, together with colleagues, made the tool AutoMIND to fit spiking network models to data.

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.