Effects of neuronal correlations on population decoding and encoding models
Name
1008758716-MIT.pdf
Description
Full printable version
Size
6.75 MB
Format
Adobe PDF
Checksum (MD5)
6f73194f4e049abb085075a6c1a7ef28
Author(s)
Patel, Ami (Ami M.)
Advisor(s)
Tomaso Poggio.
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we analyze the effect of the correlations in neural activity on the information that is encoded in and can be decoded from a population of neurons. Various noise models describing these correlations are considered - in particular, we use models that take into account the pairwise correlations and other, simpler models that assume shared global additive and/or multiplicative noise factors. The performance of these models on firing rate prediction (encoding) and population decoding are studied. Our analyses show a significant beneficial effect of pairwise correlations on encoding models, with much of this benefit being explained by the global noise models. However, the effects of correlations on decoding vary among our datasets, providing an empirical justification to the theoretical results suggesting correlations can be either helpful or harmful to decoding.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis. Thesis pagination reflects the way it was delivered to the Institute Archives and Special Collections, MIT.
Includes bibliographical references (pages 71-73).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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