Explore the intricacies of neural computation in this lecture from the Brain and Computation Boot Camp. Delve into the complexities of neuronal models, starting with the historically dominant point neuron and progressing to more sophisticated representations. Examine the Purkinje cell and its role in the cerebellum, understanding the linear computational algorithm involved. Investigate the challenges posed by long, thin dendrites and their impact on voltage subunits. Learn about NMDA spikes and their occurrence in awake animals, including interneurons. Analyze experimental evidence supporting the compartmentalization of dendritic spikes and the two-layer hypothesis. Gain insights into the biophysics of neural computation and its implications for understanding brain function.
Overview
Syllabus
Intro
What's the input-output rule?
The Question: How complicated a model do we need
Historicaly, the point neuron has been the dominant model
The Purkinje Cell
The Cerebellum
The Linear Computational Algorithm of Cerebellar
A progression of models
Problem 1: Long thin dendrites separated by larger-diameter structures provide numerous wel-isolated voltage subunits
Digression: How NMDA Spikes work
Dendritic spikes...in awake animals
Even interneurons generate NMDA spikes!
Direct evidence that dendritic spikes really are well compartmentalized
Experimental test of the 2-layer hypothesis
Taught by
Simons Institute
