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.