Modeling inhibition-mediated neural dynamics in the rodent spatial navigation system
AuthorLyttle, David Nolan
Lin, Kevin K.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThe work presented in this dissertation focuses on the use of computational and mathematical models to investigate how mammalian brains construct and maintain stable representations of space and location. Recordings of the activities of cells in the hippocampus and entorhinal cortex have provided strong, direct evidence that these cells and brain areas are involved in generating internal representations of the location of an animal in space. The emphasis of the first two portions of the dissertation are on understanding the factors that influence the scale and stability of these representations, both of which are important for accurate spatial navigation. In addition, it is argued in both cases that many of the computations observed in these systems emerge at least in part as a consequence of a particular type of network structure, where excitatory neurons are driven by external sources, and then mutually inhibit each other via interactions mediated by inhibitory cells. The first contribution of this thesis, which is described in chapter 2, is an investigation into the origin of the change in the scale of spatial representations across the dorsoventral axis of the hippocampus. Here it will be argued that this change in scale is due to increased processing of nonspatial information, rather than a dorsoventral change in the scale of the spatially-modulated inputs to this structure. Chapter 3 explores the factors influencing the dynamical stability of class of pattern-forming networks known as continuous attractor networks, which have been used to model various components of the spatial navigation systems, including head direction cells, place cells, and grid cells. Here it will be shown that network architecture, the amount of input drive, and the timescales at which cells interact all influence the stability of the patterns formed by these networks. Finally, in chapter 4, a new technique for analyzing neural data is introduced. This technique is a spike train similarity measure designed to compare spike trains on the basis of shared inhibition and bursts.
Degree ProgramGraduate College