On construction and clinical use of multipurpose neuron models - with Etay Hay - #27
Theoretical Neuroscience PodcastShow Notes
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.
More from Theoretical Neuroscience Podcast
For neurons to function, the appropriate ion-channel proteins must be present where they are needed, including in distal dendrites and axon terminals. This requires energy, and the guest's group has developed a mechanistic mathematical model to investigate how neurons can reduce this cost by optimizing where proteins are synthesized within the cell—locally or at more distant sites. The model's predictions agree with experimental findings, suggesting that energy optimization is a fundamental operating principle of neurons.
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.
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.