Date of Award

Fall 10-31-2013

Embargo Period

6-4-2014

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Center for the Neural Basis of Cognition

Advisor(s)

Nathan Urban

Abstract

For decades electrophysiologists have recorded and characterized the biophysical properties of a rich diversity of neuron types. This diversity of neuron types is critical for generating functionally important patterns of brain activity and implementing neural computations. In this thesis, I developed computational methods towards quantifying neuron diversity and applied these methods for understanding the functional implications of within-type neuron variability and across-type neuron diversity.

First, I developed a means for defining the functional role of differences among neurons of the same type. Namely, I adapted statistical neuron models, termed generalized linear models, to precisely capture how the membranes of individual olfactory bulb mitral cells transform afferent stimuli to spiking responses. I then used computational simulations to construct virtual populations of biophysically variable mitral cells to study the functional implications of within-type neuron variability. I demonstrate that an intermediate amount of intrinsic variability enhances coding of noisy afferent stimuli by groups of biophysically variable mitral cells. These results suggest that within-type neuron variability, long considered to be a disadvantageous consequence of biological imprecision, may serve a functional role in the brain.

Second, I developed a methodology for quantifying the rich electrophysiological diversity across the majority of the neuron types throughout the mammalian brain. Using semi-automated text-mining, I built a database, Neuro- Electro, of neuron type specific biophysical properties extracted from the primary research literature. This data is available at http://neuroelectro.org, which provides a publicly accessible interface where this information can be viewed. Though the extracted physiological data is highly variable across studies, I demonstrate that knowledge of article-specific experimental conditions can significantly explain the observed variance. By applying simple analyses to the dataset, I find that there exist 5-7 major neuron super-classes which segregate on the basis of known functional roles. Moreover, by integrating the NeuroElectro dataset with brain-wide gene expression data from the Allen Brain Atlas, I show that biophysically-based neuron classes correlate highly with patterns of gene expression among voltage gated ion channels and neurotransmitters. Furthermore, this work lays the conceptual and methodological foundations for substantially enhanced data sharing in neurophysiological investigations in the future.

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