Understanding the Form and Function of Neuronal Physiological Diversity
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| Titel: | Understanding the Form and Function of Neuronal Physiological Diversity |
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| Autoren: | Shreejoy J. Tripathy |
| Publikationsjahr: | 2018 |
| Bestand: | KiltHub Research from Carnegie Mellon University |
| Schlagwörter: | Neurosciences not elsewhere classified, neuron diversity, neuron coding, stimulus decoding, olfactory bulb, neurophysiology, text mining, Neuroscience |
| Beschreibung: | 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 ... |
| Publikationsart: | thesis |
| Sprache: | unknown |
| Relation: | https://figshare.com/articles/thesis/Understanding_the_Form_and_Function_of_Neuronal_Physiological_Diversity/6724145 |
| DOI: | 10.1184/r1/6724145.v1 |
| Verfügbarkeit: | https://doi.org/10.1184/r1/6724145.v1 https://figshare.com/articles/thesis/Understanding_the_Form_and_Function_of_Neuronal_Physiological_Diversity/6724145 |
| Rights: | In Copyright |
| Dokumentencode: | edsbas.E2B65044 |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | 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 ... |
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| DOI: | 10.1184/r1/6724145.v1 |