Figure 4.

Models for motion detection occupy a continuum related to Marr & Poggio’s (1976) levels of analysis. The computational level of understanding reflects what a circuit does to promote the animal’s survival. In flies, motion detection stabilizes orientation and walking speed during navigation, among other functions. The algorithmic level reflects a mathematical summary of the computation, in this case, a correlator model, which explains fly rotational behavior very well in many, but not all, circumstances (Hassenstein & Reichardt 1956). This algorithm can be split into processing steps, which yields insight into the computation and leads to models that are progressively closer to what may be implemented in the circuit. In the figure, a linear–nonlinear model (Leong et al. 2016) and a split ON–OFF set of computations (Fitzgerald & Clark 2015, Salazar-Gatzimas et al. 2018) can be equivalent to the correlator model under some limits. Finally, the biological mechanism reflects the actual biophysical and circuit processes that implement the higher-level descriptions. In the figure, specific input neurons change conductances in a direction-selective T4 cell, a model that reduces to a correlator model with small inputs (Zavatone-Veth et al. 2020). V_m represents the T4 membrane voltage; Mi9, Mi1, and Mi4 are classes of neurons providing input to T4 at different retinotopic offsets. Images of processing steps taken from Fitzgerald & Clark (2015) (CC BY 4.0).