Simulation-based inference for efficient identification of generative models in computational connectomics
Fig 2
Formulating wiring rules in the rat barrel cortex as simulation-based models.
(A) The structural model of the rat barrel cortex contains digital reconstructions of position, morphology, and subcellular features of several neuron types in the barrel cortex and the ventral posterior medial nucleus (VPM) of the thalamus. (B) We formulate a wiring rule that predicts the probability of a synapse between two neurons from their dense structural overlap (DSO), i.e., the product of the number of pre- and postsynaptic structural features, normalized by all postsynaptic features in a given subvolume (postAll). (C) By applying the wiring rule to every neuron-pair subvolume combination of the model to connection probabilities and then sampling corresponding synapse counts from a Poisson distribution (left), we can simulate a barrel cortex connectome. To compare the simulated data to measurements, we calculate population connection probabilities between VPM and barrel cortex cell types as they have been measured experimentally (right). (D) To obtain a simulation-based model, we introduce parameters to the rule and define a prior distribution (left) such that each parameter combination corresponds to a different rule configuration and leads to different simulated connection probabilities (right, grey; measured data in black, [34, 35]).