Computational models of neuronal fear and addiction circuits

Abstract

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] 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.