Computational models of neuronal fear and addiction circuits

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Computational Neuroscience provides tools to abstract and generalize principles of neuronal functions using mathematics, with applicability to the entire neuroscience spectrum. Subcircuits related to fear and addiction are considered at three levels, network, cellular and intracellular levels. In the area of fear learning, we developed biophysically realistic network models for two regions of the fear circuit. We first developed a computational network model of the lateral amygdala (LA) region, and investigated how two different types of cell populations formed in LAd after auditory fear conditioning. Next, we developed a computational model of another critical element of the fear circuit, the prelimbic cortex and linked it with a model of the basal amygdala, to investigate how these two structures worked together to modulate fear expression. Since malfunction in the fear circuit is thought to underlie the pathology of post traumatic stress (PTSD) and other anxiety disorders, such models could potentially provide ideas and approaches for the development of new medications. For cocaine addiction, we developed a cellular level model of neurotransmitter homeostasis around a cortico-accumbal synapse which undergoes enduring changes after drug abuse. We then propose ideas for the development of the associated intracellular pathways for such synapses. Understanding the mechanisms involved in neurotransmitter homeostasis and in LTP/LTD can shed light on the specific targets for potential development of effective pharmacotherapy for cocaine addiction.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Electrical and computer engineering (MU)

Grantor dc:publisher

University of Missouri--Columbia

Year dc:date.issued

2011

Author dc:creator

• Pendyam, Sandeep

Advisor dc:contributor.advisor

• Nair, Satish S., 1960-

• neuronal functions
• subcircuits
• computational modeling
• anxiety disorder

Statement dc:rights

• Access to files is limited to the University of Missouri--Columbia with SSO login.

Language dc:language.iso

eng, English

DOI

https://doi.org/10.32469/10355/14516

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/14516