Information Processing and Energy Dissipation in Neurons
| dc.contributor.author | McIntosh, Lane | |
| dc.contributor.instructor | Still, Susanne | |
| dc.date.accessioned | 2013-07-02T23:35:04Z | |
| dc.date.available | 2013-07-02T23:35:04Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | We investigate the relationship between thermodynamic and information theoretic inefficiencies in an individual neuron model, the adaptive exponential integrate-and-fire neuron. Recent work has revealed that minimization of energy dissipation is tightly related to optimal information processing, in the sense that a system has to compute a maximally predictive model. In this thesis we justify the extension of these results to the neuron and quantify the neuron’s thermodynamic and information processing inefficiencies. | |
| dc.format.extent | 64 pages | |
| dc.identifier.uri | http://hdl.handle.net/10125/29510 | |
| dc.language.iso | en-US | |
| dc.publisher | University of Hawaii at Manoa | |
| dc.rights | All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner. | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.title | Information Processing and Energy Dissipation in Neurons | |
| dc.type | Thesis | |
| dc.type.dcmi | Text | |
| local.thesis.degreelevel | MA | |
| local.thesis.department | Mathematics |