Variability and control of activity in small neural networks: Effects of neuron feedback dynamics

Hooper, Ryan Michael

Title:

Variability and control of activity in small neural networks: Effects of neuron feedback dynamics

dc.contributor.advisor	Prinz, Astrid A.
dc.contributor.author	Hooper, Ryan Michael
dc.contributor.committeeMember	Butera, Robert J.
dc.contributor.committeeMember	Canavier, Carmen C.
dc.contributor.committeeMember	Cymbalyuk, Gennady S.
dc.contributor.committeeMember	Jaeger, Dieter
dc.contributor.department	Biomedical Engineering (Joint GT/Emory Department)
dc.date.accessioned	2017-06-07T17:37:05Z
dc.date.available	2017-06-07T17:37:05Z
dc.date.created	2016-05
dc.date.issued	2016-01-15
dc.date.submitted	May 2016
dc.date.updated	2017-06-07T17:37:05Z
dc.description.abstract	Rhythmic neural networks are dynamic systems that reliably generate stereotyped activity that drives numerous biological processes essential to life, including motor pattern generation. Due to these networks’ reliable pattern generation, as well as the broad wealth of insights into fundamental questions in neuroscience that have been gained in their study without considering their fundamentally stochastic nature, the variability in their pattern generation is often overlooked. But such rhythmic networks are typically composed of a richly diverse ensemble of neurons, synapses, and their underlying properties and kinetics, each of which possesses individual dynamics that interact to contribute to the collective network dynamics that determine not just steady-state neural network activity, but also the presence or absence of network reliability and stability in the face of perturbations and stochastic processes. Because the crustacean stomatogastric network is a well studied and understood network, is experimentally amenable, and has been modeled extensively, it serves as a good system for investigating the role specific features of network composition play in determining network activity variability. Advances here may readily be adapted to inform models that are currently the focus of intense study aimed at gaining an understanding of the connection between underlying molecular and genetic cell properties and rhythmic neural network activity. The primary focus of this research is to explore the impacts of one such feature of network composition that is involved in stochastic network activity—the dynamics of synaptic feedback—and in turn determining its impact on variability of the pacemaker network. We have discovered that synaptic feedback dynamics in the crustacean stomatogastric pattern generating network tend to be ordered in multiple senses that optimally minimize rhythmic variability: in terms of both feedback neuron phase response properties, and cycle-by-cycle phase maintenance of synaptic feedback burst width. Our findings have implications for neural network design and optimization as well as neural network model and database construction.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/58156
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Phase response curve
dc.subject	Phase response analysis
dc.subject	Hybrid networks
dc.subject	Post inhibitory rebound
dc.subject	Neural network variability
dc.subject	Network feedback
dc.subject	Neural networks
dc.subject	Dynamic clamp
dc.title	Variability and control of activity in small neural networks: Effects of neuron feedback dynamics
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.corporatename	Wallace H. Coulter Department of Biomedical Engineering
local.contributor.corporatename	College of Engineering
relation.isOrgUnitOfPublication	da59be3c-3d0a-41da-91b9-ebe2ecc83b66
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral