Dendritic biophysics and evolution

The biophysical features of neurons are the building blocks of computation in the brain. Dendrites are the physical site of the vast majority of synaptic connections and can expand the information processing capabilities of neurons. Due to their complex morphological attributes and various ion channels, dendrites shape how thousands of inputs are integrated into behaviorally-relevant outputs at the level of individual neurons. However, several long-standing issues limit our understanding of dendritic biophysics. In addition to distorted electrophysiological measurements, prior studies have largely been limited to ex vivo preparations from rodent animal models, providing little insight for computation in the awake human brain. In this thesis, we overcome these limitations to provide new insights on biophysics at the intersection of dendritic morphology and evolution. In chapter 1, we demonstrate that voltage-clamp analysis, which was employed to derive much of our understanding of synaptic transmission, is incompatible with most synapses because they reside on electrically-compartmentalized spines. We also develop new approaches to provide accurate measurements of synaptic strength. Then, in chapter 2, we directly correlate somatic and distal dendritic activity in the awake mouse visual cortex to show an unexpectedly high degree of coupling in vivo. In chapter 3, we perform dendritic recordings in large human neurons to reveal distinct integrative properties from commonly studied rat neurons. Finally, in chapter 4, we characterize neurons in 10 mammalian species to extract evolutionary rules governing neuronal biophysics and uncover human specializations. Together, these four thesis projects expand our understanding of the influence of dendritic geometry and evolution on neuronal biophysics.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Beaulieu-Laroche, Lou.

Advisor dc:contributor.advisor

• Mark Harnett.

• Brain and Cognitive Sciences.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/130812

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/130812