A Biologically Realistic Network Model of Acquisition and Extinction of Conditioned Fear Associations in Lateral Amygdala Neurons

Abstract

The basolateral amygdala plays an important role in the acquisition and expression of both fear conditioning and fear extinction. To understand how a single structure could encode these "opposite" memories, we developed a biophysical network model of the lateral amygdala (LA) neurons during auditory fear conditioning and extinction. Membrane channel properties were selected to match waveforms and firing properties of pyramidal cells and interneurons in LA, from published in vitro studies. Hebbian plasticity was implemented in excitatory AMPA and inhibitory GABA(A) receptor-mediated synapses to model learning. The occurrence of synaptic potentiation versus depression was determined by intracellular calcium levels, according to the calcium control hypothesis. The model was able to replicate conditioning- and extinction-induced changes in tone responses of LA neurons in behaving rats. Our main finding is that LA activity during both acquisition and extinction can be controlled by a balance between pyramidal cell and interneuron activations. Extinction training depressed conditioned synapses and also potentiated local interneurons, thereby inhibiting the responses of pyramidal cells to auditory input. Both long-term depression and potentiation of inhibition were required to initiate and maintain extinction. The model provides insights into the sites of plasticity in conditioning and extinction, the mechanism of spontaneous recovery, and the role of amygdala NMDA receptors in extinction learning.

Neuroanatomical Structures Underlying the Extinction of Drug-Seeking Behavior

“…Conversely, projections from the ILC excite GABAergic neurons in the intercalated [265] cell masses inhibiting the CE thereby promoting the extinction of conditioned fear [266]. The expression of cocaine-and heroin-seeking behavior involves projections from the PrLC to the AcbC [267][268][269][270][271][272], whereas projections from the ILC to the AcbSh mediate the extinction of cocaine-seeking behavior [137].…”

“…Future research should also examine the influence of different neurotransmitter systems on extinction learning. For example, within the PFC, activation of the cannabinoid CB1 receptors facilitates the extinction of conditioned fear whereas inhibition of the same receptor attenuates extinction of conditioned fear [266]. Determining the role and amount of impact that agonists acting at CB1, glutamate, or GABA receptor sites have on the extinction of drug-associated memories may help answer important questions regarding the neuroanatomical circuits underlying the extinction of drug-associated memories.…”

Auditory Fear Circuits in the Amygdala – Insights from Computational Models

“…As cited, the single cell properties should match those reported in biology, including the membrane potential responses to various current injections. Once such single cell models are developed (see Li et al, 2009), they can be embedded into networks. In our simple two-cell network model, both the pyramidal cell and the interneuron received direct afferent tone/shock (CS/US) inputs via synaptic connections.…”

“…Given the central role of LA in the acquisition and expression of fear memory, it has been proposed that this structure may be a site of inhibition in extinction (e.g., Hobin et al, 2003). The motivation of the Li et al (2009) study was to determine how LA might acquire and store both conditioning and extinction memories related to auditory fear. After a review of the development of the model, and its validation, we discuss the unique insights provided by the model.…”

“…The occurrence of synaptic potentiation versus depression was determined by intracellular calcium levels, according to the calcium control hypothesis. Details related to the equations can be found in Li et al (2009). Learning of conditioned fear leads to changes in synaptic strength in the neural circuitry and the magnitude and sign of these variations are unique insights that a computational model can provide.…”