title: Active Inference and Learning in the Cerebellum.
abstract: This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception.
authors.affiliation: Wellcome Trust Centre for Neuroimaging, UCL, London WC1N 3BG, U.K. k.friston@ucl.ac.uk.
authors.forename: Karl
authors.initials: K
authors.lastname: Friston
authors.affiliation: Catalan Institute of Advanced Research, Technology Department, Universitat Pompeu Fabra, Barcelona 08018, Spain ivanherreros@gmail.com.
authors.forename: Ivan
authors.initials: I
authors.lastname: Herreros
mesh_terms: D001499:Bayes Theorem; D001767:Blinking; D002531:Cerebellum / Q000502:physiology*; D003214:Conditioning, Classical; D006801:Humans; D007858:Learning; D008962:Models, Theoretical