A spiking neuron model of pharmacologically-biased fear conditioning in the amygdala

SfN Abstracts, 2019

Peter Duggins, Chris Eliasmith


Network models that reconstruct (a) the dynamics of individual neurons, (b) the anatomy of specific brain regions, and (c) the behaviors governed by these regions are important for understanding mental disorders and their pharmacological treatment. We present a spiking neuron model of the rat amygdala that undergoes fear conditioning, and is appropriately modulated by simulated pharmacological perturbation (including oxytocin OXY, seratonin 5-HT, dopamine DA, and muscimol MUSC). The network includes neural populations for the central-lateral (CeL), central-medial (CeM), and basolateral (BLA) amygdala; interneurons in BLA and CeL; inputs from spinal cord, cortex and hippocampus; and motor output through the periaqueductal gray (PAG). The model is trained by pairing negative stimuli (footshocks) with neutral stimuli (auditory tones) within a prescribed context (conditioning cage). Prediction error signals drive associative learning of synaptic connection weights in CeL and BLA. Following an experimentally-vetted training regime, the model exhibits the fear response (freezing, via CeM inhibition of tonically driven PAG) to presentation of the conditioned tone or context (Fig. 1). Furthermore, repeatedly presenting tones without shocks in a new context causes extinction of the fear response in that context, but not in others, via synaptic plasticity in BLA. To simulate pharmacology, we excite particular neural subpopulations that are known to express receptors for the corresponding neurotransmitters. Fig. 2 reports mean freezing in response to twelve pharmacological manipulations, applied during various stages of conditioning, extinction, or expression. Simulated freezing is consistent with empirical data from rats injected with appropriate agonists or antagonists. These results demonstrate that the mechanisms underlying fear conditioning, including associative learning, extinction, and pharmacology, can be understood through the dynamic interactions between amygdala nuclei. Extensions to the model will allow targeted biophysical manipulations and anatomical reconstructions of human amygdala; experimental predictions made from this model may profitably inform human pharmacology and the treatment of conditions such as post-traumatic stress disorder.

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SfN Abstracts
Chicago USA


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