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Type of Document	Dissertation
Author	Mazor, Ofer
URN	etd-06062005-113150
Persistent URL	http://resolver.caltech.edu/CaltechETD:etd-06062005-113150
Title	Neural dynamics and population coding in the insect brain
Degree	PhD
Option	Computation and Neural Systems
Advisory Committee	Advisor Name Title Pietro Perona Committee Chair Athanassios Siapas Committee Member Gilles J. Laurent Committee Member Masakazu Konishi Committee Member Michael Dickinson Committee Member
Keywords	temporal coding olfaction insect locust spike-sorting population coding
Date of Defense	2005-05-24
Availability	unrestricted
Abstract Sensory information is represented in the brain through the activity of populations of neurons. How this information is encoded and how it is processed and read out are crucial questions in neuroscience. The work presented here examines these issues using an insect brain model system. Specifically, this work addresses how odor information is represented across a population of neurons in this relatively simple nervous system. It asks how the dynamics of a population of neurons contribute to the encoding of information. To address these questions, simultaneous multi-unit extracellular recordings were made in vivo in the locust brain. The first part of the dissertation describes several advances in spike-sorting methods that were necessary for analyzing such recordings. These advances include quantitative tests of sorting quality, and they allow for automated spike-sorting. Using these techniques, data sampled from tens of neurons over hours of recording can be analyzed with relative ease. The remainder of the dissertation examines the encoding of olfactory information by a population of neurons called projection neurons (PNs), located in the first olfactory relay of the brain. Odor information is shown to be represented by a subpopulation of responsive PNs. The composition of this population changes over time in an odor-specific manner, thus forming a distributed, dynamical representation. The statistics of this response and its dynamics are quantified. Furthermore, the mechanism by which odor information is extracted from the PN population response is examined. A second set of recordings were made from Kenyon cells (KCs), which receive direct excitatory synaptic input from PNs. The dynamic response of the PN population appears to be decoded by KCs through a mechanism based on several underlying components, including oscillatory dynamics, feed-forward inhibition, and intrinsic properties of the KCs. This decoding process is shown to drastically change the odor representations, from dense to sparse. Taken together, the results presented in this dissertation establish that the complex spatial and temporal dynamics of the PN population do encode odor information, and that this information is decoded by other neurons (KCs) in a very precise way, resulting in a drastic transformation of representation. The basic mechanisms underlying this transformation exist in many brain areas and across phyla, suggesting that many of the principles described here could be of general relevance.
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