The Interplay of Architecture and Correlated Variability in Neuronal Networks
| dc.contributor.advisor | Josić, Krešimir | |
| dc.contributor.committeeMember | Ott, William | |
| dc.contributor.committeeMember | Török, Andrew | |
| dc.contributor.committeeMember | Dabaghian, Yuri | |
| dc.creator | Trousdale, James Russell 1986- | |
| dc.date.accessioned | 2013-12-02T23:05:23Z | |
| dc.date.available | 2013-12-02T23:05:23Z | |
| dc.date.created | August 2013 | |
| dc.date.issued | 2013-08 | |
| dc.date.updated | 2013-12-02T23:05:30Z | |
| dc.description.abstract | This much is certain: neurons are coupled, and they exhibit covariations in their output. The extent of each does not have a single answer. Moreover, the strength of neuronal correlations, in particular, has been a subject of hot debate within the neuroscience community over the past decade, as advancing recording techniques have made available a lot of new, sometimes seemingly conflicting, datasets. The impact of connectivity and the resulting correlations on the ability of animals to perform necessary tasks is even less well understood. In order to answer relevant questions in these categories, novel approaches must be developed. This work focuses on three somewhat distinct, but inseparably coupled, crucial avenues of research within the broader field of computational neuroscience. First, there is a need for tools which can be applied, both by experimentalists and theorists, to understand how networks transform their inputs. In turn, these tools will allow neuroscientists to tease apart the structure which underlies network activity. The Generalized Thinning and Shift framework, presented in Chapter 4, addresses this need. Next, taking for granted a general understanding of network architecture as well as some grasp of the behavior of its individual units, we must be able to reverse the activity to structure relationship, and understand instead how network structure determines dynamics. We achieve this in Chapters 5 through 7 where we present an application of linear response theory yielding an explicit approximation of correlations in integrate--and--fire neuronal networks. This approximation reveals the explicit relationship between correlations, structure, and marginal dynamics. Finally, we must strive to understand the functional impact of network dynamics and architecture on the tasks that a neural network performs. This need motivates our analysis of a biophysically detailed model of the blow fly visual system in Chapter 8. Our hope is that the work presented here represents significant advances in multiple directions within the field of computational neuroscience. | |
| dc.description.department | Mathematics, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10657/476 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Neurosciences | |
| dc.subject | Neurons | |
| dc.subject | Correlations | |
| dc.subject | Point processes | |
| dc.subject | Spiking processes | |
| dc.subject | Coupling | |
| dc.subject | Cross-correlations | |
| dc.subject | Cross-cumulants | |
| dc.subject | Cumulants | |
| dc.subject | Computational neuroscience | |
| dc.subject | Fly vision | |
| dc.subject.lcsh | Mathematics | |
| dc.title | The Interplay of Architecture and Correlated Variability in Neuronal Networks | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Mathematics, Department of | |
| thesis.degree.discipline | Mathematics | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |