Theoretical Neuroscience Podcast

The prominent and colorful neuroscientist Valentino Braitenberg was born 100 years ago.
He co-founded the Max Planck Institute of Biological Cybernetics in Tübingen in Germany, where he made seminal contributions to neuroanatomy, synthetic psychology, and theories for cerebellar, fly vision and cortical function.
He was celebrated at the recent Braitenberg*100 symposium which I attended together with today's guest.
Ad Aertsen is an outstanding computational neuroscientist and worked with Braitenberg back in the days.

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.