Model Based Inference of Large Scale Brain Networks with Approximate Bayesian Computation

Abstract

19Brain networks and the dynamics that unfold upon them are of vital importance at many scales of 20 systems neuroscience. The parameterization of generative models from empirical data (inverse 21 modelling) has become of great utility to theoretical understanding in this domain. However, it has 22 become difficult to infer mechanisms of spontaneous neural activity with existing tools due to the 23 adoption of simplifying model assumptions that appeal to steady-state approximations of neural 24 dynamics. In this work we describe a framework employing Approximate Bayesian Computation 25 (ABC) for the inversion of neural models that can be used to flexibly model any statistical feature of 26 empirically recorded data without assuming any specific form of dynamics. Further, we demonstrate 27 how Bayesian model comparison can be applied to ABC fitted models to facilitate the selection of 28 competing dynamical hypotheses. This work provides a validation of this approach in the context of a 29 West et al. (2019): Mechanistic Inference of Brain Dynamics with ABC bioRxiv_v1.0 27/09/2019 preprint_v1.02 Parkinsonian electrophysiology, using a set of multisite recordings from Parkinsonian rats to 1 demonstrate convergence, consistency, and uniqueness of parameter estimation; face validity of the 2 model comparison procedures; and its utility in answering biologically relevant problems. From the 3 validation and example applications presented here we conclude that the proposed framework is likely 4 to be of value to a wide range of researchers aiming to explain how brain dynamics emerge from neural 5 circuits. 6