Flying insects are valuable animal models for elucidating computational processes underlying
visual motion detection. For example, optical flow analysis by wide-field motion
processing neurons in the insect visual system has been investigated from both behavioral
and physiological perspectives [1]. This has resulted in useful computational models with
diverse applications [2,3]. In addition, some insects must also extract the movement of
their prey or conspecifics from their environment. Such insects have the ability to detect
and interact with small moving targets, even amidst a swarm of others [4,5]. We use electrophysiological
techniques to record from small target motion detector (STMD) neurons
in the insect brain that are likely to subserve these behaviors. Inspired by such recordings,
we previously proposed an ‘elementary’ small target motion detector (ESTMD) model
that accounts for the spatial and temporal tuning of such neurons and even their ability
to discriminate targets against cluttered surrounds [6-8]. However, other properties such
as direction selectivity [9] and response facilitation for objects moving on extended trajectories
[10] are not accounted for by this model. We therefore propose here two model
variants that cascade an ESTMD model with a traditional motion detection model algorithm,
the Hassenstein Reichardt ‘elementary motion detector’ (EMD) [11]. We show
that these elaborations maintain the principal attributes of ESTMDs (i.e. spatiotemporal
tuning and background clutter rejection) while also capturing the direction selectivity
observed in some STMD neurons. By encapsulating the properties of biological STMD
neurons we aim to develop computational models that can simulate the remarkable capabilities
of insects in target discrimination and pursuit for applications in robotics and
artificial vision systems.
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