Heidelberg University

Bridging scales in Neuroscience: from biophysics to populations

Eilif Muller, EPFL - Blue Brain Project, Geneva

Abstract:

Paraphrasing Richard Feynman, what we cannot create we don't understand. In this vein, the Blue Brain Project (BBP) has developed what has been called the most complete simulation of a piece of brain tissue to date [1,2], starting from models of the biophysics of individual neurons and synapses. While it is the end of a 10 year journey for the BBP, it is also a great beginning. It is stunning it its anatomical and physiological complexity, and far unrivaled in the extent it is constrained by neuroscience experiments, making it a great place to start understanding the brain.

To this end, it could be said that we must simultaneously appreciate its daunting complexity, and grasp its essential mechanisms. There remain major barriers to achieving the latter, an integrated and synthesized view of experimental facts pertinent for the functional principles of brain systems. A method for synthesizing the BBP neocortical model to a minimal form is, thus formulated, an ill-posed problem. Minimal for what purpose?

Fundamental outstanding questions about the brain span scales of description. How do ion channel mutations cause migraines? Why are there fast (AMPA), slow (NMDA) and slower synaptic receptors? Why do pyramidal cells have apical dendrites? What is the role of martinotti neurons? Why does cortex have 6 layers? What is the neuronal code? What is signal, what is noise? What is epilepsy? How do microcircuits process and reorganize to store information? Does cortex implement a canonical computation? What is the role of neuromodulators? What is the role of the cortical hierarchy? How does cortex interact with the hippocampus to implement memory and navigation? What goes awry in schizophrenia? It is unlikely that one level of description will grant insights into all these questions.

Starting with the biophysics of neurons and synapses, in this course I will span the scales of neuronal descriptions up to population equations, and conclude with a review of cutting edge research on synaptic plasticity, believed to be the substrate of learning and memory.


[1] Markram et al, 2015
[2] Koch and Buice, 2015


Part 1 - Biophysics of neurons and synapses
- Ion channels
- Cable theory
- A cortical pyramidal neuron model
- Modeling synaptic transmission
- Some principles of coding in single neurons

Part 2 - Reconstruction and simulation of neocortical microcircuitry
- Markram et al, 2015 in detail.

Part 3 - From morphologies to point neurons to population equations
- Quantitative simplification to point neuron microcircuit models
- Derivation of population density equations from point neurons

Part 4 - Biophysical models of plasticity
- Calcium-based plasticity models
- Microcircuit rewiring
- Dendrites and plasticity
- Open problems & wild speculations