Role of sensory input distribution and intrinsic connectivity in lateral amygdala during auditory fear conditioning: a computational study.
We propose a novel reduced-order neuronal network modeling framework that includes an enhanced firing rate model and a corresponding synaptic calcium-based synaptic learning rule. Specifically, we propose enhancements to the Wilson-Cowan firing-rate neuron model that permit full spike-frequency adaptation seen in biological lateral amygdala (LA) neurons, while being sufficiently general to accommodate other spike-frequency patterns. We also report a technique to incorporate calcium-dependent plasticity in the synapses of the network using a regression scheme to link firing rate to postsynaptic calcium. Together, the single-cell model and the synaptic learning scheme constitute a general framework to develop computationally efficient neuronal networks that employ biologically realistic synaptic learning. The reduced-order modeling framework was validated using a previously reported biophysical conductance-based neuronal network model of a rodent LA that modeled features of Pavlovian conditioning and extinction of auditory fear (Li et al., 2009). The framework was then used to develop a larger LA network model to investigate the roles of tone and shock distributions and of intrinsic connectivity in auditory fear learning. The model suggested combinations of tone and shock densities that would provide experimental estimates of tone responsive and conditioned cell proportions. Furthermore, it provided several insights including how intrinsic connectivity might help distribute sensory inputs to produce conditioned responses in cells that do not directly receive both tone and shock inputs, and how a balance between potentiation of excitation and inhibition prevents stimulus generalization during fear learning.
Department of Electrical & Computer Engineering, University of Missouri, Columbia, MO 65211, United States.
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Ball
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Hummos
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Nair
D000161:Acoustic Stimulation; D000679:Amygdala / Q000502:physiology*; D000818:Animals; D001185:Artificial Intelligence; D003214:Conditioning, Classical / Q000502:physiology*; D008959:Models, Neurological; D008962:Models, Theoretical; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology; D051381:Rats