The capacity for memory is one of the most profound features of the mammalian brain, and the proper encoding and retrieval of information are the processes that form the basis of learning. The goal of this thesis is to further our understanding of the network-level mechanisms supporting learning and memory in the mammalian brain.
The hippocampus has been long recognized to play a central role in learning and memory. Although being one of the most extensively studied structures in the brain, the precise circuit mechanisms underlying its function remain elusive. Principal cells in the hippocampus form complex representations of an animal's environment, but in stark contrast to the interneuron population -- and despite the apparent need for functional segregation -- these cells are largely considered a homogeneous population of coding units. Much work, however, has indicated that principal cells throughout the hippocampus, from the input node of the dentate gyrus to the output node of area CA1, differ developmentally, genetically, anatomically, and functionally.
By employing in vivo two-photon calcium imaging in awake, behaving mice, we attempted to
characterize the role of dened subpopulations of neurons in memory-related behaviors. In the
first part of this thesis, we focus on the dentate gyrus input node of the hippocampus. Chapter 2 compares the functional properties of adult-born and mature granule cells. Chapter 3 expands on this work by comparing granule cells with mossy cells, another glutamatergic but relatively understudied cell type. The second part of this thesis focuses on the hippocampal output node, area CA1. In chapter 4, we characterize an inhibitory microcircuit that differentially targets the sublayers of area CA1. And in chapter 5, we directly compare the contributions of these sublayers to episodic and semantic memory.
Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8474BC8 |
Date | January 2016 |
Creators | Danielson, Nathan B. |
Source Sets | Columbia University |
Language | English |
Detected Language | English |
Type | Theses |
Page generated in 0.0015 seconds