Dynamic divisive normalization circuits explain and predict change detection in monkey area MT

Sudden changes in visual scenes often indicate important events for behavior. For their quick and reliable detection, the brain must be capable to process these changes as independently as possible from its current activation state. In motion-selective area MT, neurons respond to instantaneous speed changes with pronounced transients, often far exceeding the expected response as derived from their speed tuning profile. We here show that this complex, non-linear behavior emerges from the combined temporal dynamics of excitation and divisive inhibition, and provide a comprehensive mathematical analysis. A central prediction derived from this investigation is that attention increases the steepness of the transient response irrespective of the activation state prior to a stimulus change, and irrespective of the sign of the change (i.e. irrespective of whether the stimulus is accelerating or decelerating). Extracellular recordings of attention-dependent representation of both speed increments and decrements confirmed this prediction and suggest that improved change detection derives from basic computations in a canonical cortical circuitry.

Author summary The world is a dynamic place: visual scenes are changing rapidly, and decisions have to be taken quickly and reliably to ensure successful behavior and, finally, survival. This creates a challenge for the brain, since it has to fulfill two requirements at the same time: It has to detect changes regardless of their magnitude and sign of change, and it has to primarily focus on behaviorally relevant changes. We here studied transient stimulus-speed change responses of neurons in motion-sensitive area MT and identify a mechanism supporting both of these requirements. This mechanism can be realized by an elementary neural model circuit which closely fits physiological data. The model is based on dynamic divisive inhibition generating fast transient rate modulations in response to rapid input changes. We analyzed this circuit mathematically and arrived at the formal prediction that attention will consistently increase the steepness of the transient, irrespective of the magnitude of the pre-change activation and the sign of the input change, thus allowing for faster, reliable reaction to any attended event. By performing single-cell recordings with both stimulus accelerations and decelerations, which could either be attended or not attended, the experimental data fully confirmed the predictions from modelling.