*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Advisor:
Steve M. Potter, Ph.D (Georgia Institute of Technology, Dept. of BME)
Thesis Committee:
Robert J. Butera , Ph.D. (Georgia Institute of Technology, Dept. of BME)
Astrid A. Prinz, Ph.D. (Emory University, Dept. of Biology)
Garrett B. Stanley, Ph.D. (Georgia Institute of Technology, Dept. of BME)
Daniel A. Wagenaar, Ph.D. (California Institute of Technology, Dept. of Physics)
The majority of synaptic connections in sensory cortical areas arise locally, resulting in a highly recurrent network structure. The recurrent structure of sensory cortical networks necessitates (1) homeostatic mechanisms to maintain circuit stability and (2) network encoding procedures that make use of recurrent connectivity. Synaptic scaling, the regulation of synaptic strength in inverse relation to network activity levels, is thought to be important for stabilizing the response of sensory cortical circuits to afferent input. Although synaptic scaling is a ubiquitous feature of developing neuronal networks, the precise mechanism by which neural circuits sense and adjust their own activity levels is hotly debated. Previous studies have suggested that either spiking or neurotransmission may be sensed to trigger synaptic scaling, but current tools cannot discriminate between these possibilities since neurotransmission and spiking are causally intertwined. In addition to impacting network stability, recurrent connectivity plays a role in sensory transduction and processing. Computational studies indicate that recurrent synaptic connectivity linearizes the population firing response to input signals despite the highly nonlinear properties of constituent neurons. However, whether this linearization occurs in a biological network is unclear. The central goal of this proposal is to investigate how recurrent cortical networks (1) regulate excitatory synaptic strength and (2) encode time varying stimuli. This goal will be accomplished in three specific aims: First, I will develop a real-time multichannel electrophysiology platform capable of delivering optical stimuli to, and recording electrical activity from, dissociated cortical networks. In my second aim, I will use real-time feedback to clamp network firing levels during a pharmacological blockade of excitatory neurotransmission, so that homeostatic changes in synaptic strength can be directly attributed to the absence of neurotransmission. I hypothesize that post-synaptic neurons regulate excitatory synaptic strength in inverse proportion to the levels of excitatory neurotransmission. In my final aim, I will use the system to deliver time-varying optical stimuli to the network during various states of synaptic decoupling. I will then use information theoretic methods to deduce how recurrent connectivity affects the population encoding process. I hypothesize that the existence of recurrent connectivity linearizes the population firing rate response to time-varying stimuli, but simultaneously reduces information rates. Together, these experiments will help elucidate how homeostatic mechanisms stabilize recurrent neuronal circuitry, and how recurrent circuitry impacts network-level sensory encoding.
