Neural processing as causal inference

Perception is about making sense, that is, understanding what events in the outside world caused the sensory observations. Consistent with this intuition, many aspects of human behavior confronting noise and ambiguity are well explained by principles of causal inference. Extending these insights, recent studies have applied the same powerful set of tools to perceptual processing at the neural level. According to these approaches, microscopic neural structures solve elementary probabilistic tasks and can be combined to construct hierarchical predictive models of the sensory input. This framework suggests that variability in neural responses reflects the inherent uncertainty associated with sensory interpretations and that sensory neurons are active predictors rather than passive filters of their inputs. Causal inference can account parsimoniously and quantitatively for non-linear dynamical properties in single synapses, single neurons and sensory receptive fields.

► Neural circuits solve causal inference problems. ► Cortical circuits implement hierarchical predictive models. ► Neural variability reflects uncertainty about perceptual interpretations. ► This is a result of predictive coding in spiking neurons. ► Probabilistic inference accounts for adaptive properties of sensory neurons.