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Title:
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Selecting models that describe neural population responses |
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Author:
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Uppuluri, Jyotirmai |
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Advisor:
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Johnson, Don H. |
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Abstract:
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This research provides a new approach to analyzing the statistical properties of limited data sets. A lack of sufficient data is a pressing issue in the neuroscience community, where researchers want to understand the statistics of interactions be tween neurons. The methods used in this work involve an information-theoretic criterion for determining a class of models that can accurately describe the available neural data's statistics. These methods combine principles from Minimum Description Length coding and an approach based on the Kullback-Leibler distance. In the context of limited data, this research provides a less forced or error-prone view of the statistical properties of the data. This provides researchers in the neuroscience community with a more robust approach to data analysis. |
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Citation:
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Uppuluri, Jyotirmai. "Selecting models that describe neural population responses." Masters Thesis, Rice University, ETD http://hdl.handle.net/1911/20543. |
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Citable link to this page:
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http://hdl.handle.net/1911/20543 |
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Date:
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2007 |
