Integrated dopaminergic neuronal model with reduced intracellular processes and inhibitory autoreceptors


Dopamine (DA) is a crucial neurotransmitter for multiple brain functions, and dysfunctions of the dopaminergic system are implicated in neurological and neuropsychiatric disorders. Although the dopaminergic system has been studied at multiple levels, an integrated and economical computational model that bridges from molecular to neuronal circuit level continues to be lacking. During this study, the authors aim to develop a realistic nonetheless economical computational model of a dopaminergic pre-synaptic terminal. They initial systematically perturb the variables/substrates of a longtime computational model of DA synthesis, unleash and uptake, and based on their relative dynamical timescales and steady-state changes, approximate and cut back the model into two versions: one for simulating hourly timescale, and another for millisecond timescale. They show that the original and reduced models exhibit rather similar steady and perturbed states, whereas the reduced models are more computationally efficient and illuminate the underlying key mechanisms. They then incorporate the reduced quick model into a spiking neuronal model that may realistically simulate the spiking behaviour of dopaminergic neurons. In addition, they successfully embody autoreceptor-mediated inhibitory current explicitly within the neuronal model. This integrated computational model provides the first step toward an economical computational platform for realistic multiscale simulation of dopaminergic systems in in silico neuropharmacology.

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