Chronic use of cannabis might impair sensory error processing in the cerebellum through endocannabinoid dysregulation

Chronic use of cannabis leads to both motor deficits and the downregulation of CB1 receptors (CB1R) in the cerebellum. In turn, cerebellar damage is often related to impairments in motor learning and control. Further, a recent motor learning task that measures cerebellar-dependent adaptation has been shown to distinguish well between healthy subjects and chronic cannabis users. Thus, the deteriorating effects of chronic cannabis use in motor performance point to cerebellar adaptation as a key process to explain such deficits. We review the literature relating chronic cannabis use, the endocannabinoid system in the cerebellum, and different forms of cerebellar-dependent motor learning, to suggest that CB1R downregulation leads to a generalized underestimation and misprocessing of the sensory errors driving synaptic updates in the cerebellar cortex. Further, we test our hypothesis with a computational model performing a motor adaptation task and reproduce the behavioral effect of decreased implicit adaptation that appears to be a sign of chronic cannabis use. Finally, we discuss the potential of our hypothesis to explain similar phenomena related to motor impairments following chronic alcohol dependency

DataSheet1_Drive competition underlies effective allostatic orchestration.PDF

Living systems ensure their fitness by self-regulating. The optimal matching of their behavior to the opportunities and demands of the ever-changing natural environment is crucial for satisfying physiological and cognitive needs. Although homeostasis has explained how organisms maintain their internal states within a desirable range, the problem of orchestrating different homeostatic systems has not been fully explained yet. In the present paper, we argue that attractor dynamics emerge from the competitive relation of internal drives, resulting in the effective regulation of adaptive behaviors. To test this hypothesis, we develop a biologically-grounded attractor model of allostatic orchestration that is embedded into a synthetic agent. Results show that the resultant neural mass model allows the agent to reproduce the navigational patterns of a rodent in an open field. Moreover, when exploring the robustness of our model in a dynamically changing environment, the synthetic agent pursues the stability of the self, being its internal states dependent on environmental opportunities to satisfy its needs. Finally, we elaborate on the benefits of resetting the model’s dynamics after drive-completion behaviors. Altogether, our studies suggest that the neural mass allostatic model adequately reproduces self-regulatory dynamics while overcoming the limitations of previous models.</p