34. AT THE INTERFACE OF DETAIL AND ABSTRACTION: MODELING HETEROGENEOUS DYNAMICS AND PLASTICITY IN CORTICAL PYRAMIDALS

Department: Bioengineering
Faculty Advisor(s): Gabriel Silva

Primary Student
Name: Helen G Saad
Email: hsaad@ucsd.edu
Phone: 714-747-4292
Grad Year: 2012

Abstract
Brain function is largely determined by the interplay of hundreds to billions of neurons that are exquisitely structured and meticulously arranged in specialized modules on multiple anatomical hierarchies. Although quantitative mathematical models have proved to be a fundamental tool to assist in unraveling the mysteries of the brain, it remains unclear which level of single-neuron modeling is appropriate for this quest. For speed and ease of use, the majority of neuronal network models use single summing point models, termed point-neurons, as their building blocks. However, biological neurons are highly diverse and their different types can be determined by their extensive dendritic arbor, the latter forming the siege of the most fundamental input-output adaptive processes. Rather than ignoring dendrites or representing them as passive conduction cables, we design and implement a phenomenological neuron model that lies at the interface of detail and abstraction: the model is detailed only enough to account for dendritic heterogeneity of postsynaptic processing and location-dependent tuning of inputs, while being simple enough to serve as a building block for mathematical neuronal networks that undergo plasticity. We prove that this model, coupled with coincidence-detection mechanisms at the level of the dendritic spine, is able to faithfully reproduce the dynamics of cortical pyramidal neurons and account for a large body of seemingly contradictory experimental evidence on plasticity mechanisms that these neurons exhibit. We hope that, within this framework, we will be able to unravel some of the design principles by which the rich dendritic dynamics and multitude of plasticity mechanisms are used for maximizing and stabilizing information transmission in the fascinating building blocks of the nervous system.

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