When a human catches a ball, they estimate future target location based on the current trajectory. How animals, small and large, encode such predictive processes at the single neuron level is unknown. Here we describe small target-selective neurons in predatory dragonflies that exhibit localized enhanced sensitivity for targets displaced to new locations just ahead of the prior path, with suppression elsewhere in the surround. This focused region of gain modulation is driven by predictive mechanisms, with the direction tuning shifting selectively to match the target’s prior path. It involves a large local increase in contrast gain which spreads forward after a delay (e.g. an occlusion) and can even transfer between brain hemispheres, predicting trajectories moved towards the visual midline from the other eye. The tractable nature of dragonflies for physiological experiments makes this a useful model for studying the neuronal mechanisms underlying the brain’s remarkable ability to anticipate moving stimuli.DOI: http://dx.doi.org/10.7554/eLife.26478.001

Dragonflies detect and pursue targets such as other insects for feeding and conspecific interaction. They have a class of neurons highly specialized for this task in their lobula, the “small target motion detecting” (STMD) neurons. One such neuron, CSTMD1, reaches maximum response slowly over hundreds of milliseconds of target motion. Recording the intracellular response from CSTMD1 and a second neuron in this system, BSTMD1, we determined that for the neurons to reach maximum response levels, target motion must produce sequential local activation of elementary motion detecting elements. This facilitation effect is most pronounced when targets move at velocities slower than what was previously thought to be optimal. It is completely disrupted if targets are instantaneously displaced a few degrees from their current location. Additionally, we utilize a simple computational model to discount the parsimonious hypothesis that CSTMD1's slow build-up to maximum response is due to it incorporating a sluggish neural delay filter. Whilst the observed facilitation may be too slow to play a role in prey pursuit flights, which are typically rapidly resolved, we hypothesize that it helps maintain elevated sensitivity during prolonged, aerobatically intricate conspecific pursuits. Since the effect seems to be localized, it most likely enhances the relative salience of the most recently “seen” locations during such pursuit flights.

Insects are an excellent model system for investigating computational mechanisms evolved for the challenging task of visualising and tracking small moving targets. We examined a well categorised small target motion detector (STMD) neuron, the dragonfly centrifugal STMD 1 (CSTMD1). This neuron has an unusually slow response onset, with a time course in the order of hundreds of milliseconds. A parsimonious explanation for this slow onset would be temporal low-pass filtering. However other authors have dismissed this and instead proposed a facilitation mechanism derived from second order motion detectors. We tested the spatial locality of response to continuous motion on non-contiguous paths and found spatial discontinuities in otherwise continuous motion reset the neuronal response. We modelled an array of elementary motion detectors (EMDs) in the insect visual pathway. We found that whilst individual components of the response can be explained simply by modifying the properties of the EMDs, the neurons response considered as a whole requires further elaborations within the system such as the proposed second order motion pathway.

Dragonflies pursue and capture tiny prey and conspecifics with extremely high success rates. These moving targets represent a small visual signal on the retina and successful chases require accurate detection and amplification by downstream neuronal circuits. This amplification has been observed in a population of neurons called small target motion detectors (STMDs), through a mechanism we term predictive gain modulation. As targets drift through the neuron's receptive field, spike frequency builds slowly over time. This increased likelihood of spiking or gain is modulated across the receptive field, enhancing sensitivity just ahead of the target's path, with suppression of activity in the remaining surround. Whilst some properties of this mechanism have been described, it is not yet known which stimulus parameters modulate the amount of response gain. Previous work suggested that the strength of gain enhancement was predominantly determined by the duration of the target's prior path. Here, we show that predictive gain modulation is more than a slow build-up of responses over time. Rather, the strength of gain is dependent on the velocity of a prior stimulus combined with the current stimulus attributes (e.g. angular size). We also describe response variability as a major challenge of targetdetecting neurons and propose that the role of predictive gain modulation is to drive neurons towards response saturation, thus minimising neuronal variability despite noisy visual input signals.

Dragonflies pursue and capture tiny prey and conspecifics with extremely high success rates. These moving targets represent a small visual signal on the retina and successful chases require accurate detection and amplification by downstream neuronal circuits.This amplification has been observed in a population of neurons called Small Target Motion Detectors (STMDs), through a mechanism we termed predictive gain modulation. As targets drift through the receptive field responses build slowly over time. This gain is modulated across the receptive field, enhancing sensitivity just ahead of the target's path, with suppression of activity elsewhere in the surround. Whilst some properties of this mechanism have been described, it is not yet known which stimulus parameters are required to generate this gain modulation. Previous work suggested that the strength of gain enhancement was predominantly determined by the duration of the target's prior path. Here we show that the predictive gain modulation is more than a sluggish build-up of gain over time. Rather, gain is dependent on both past and present parameters of the stimulus. We also describe response variability as a major challenge of target detecting neurons and propose that the predictive gain modulation's role is to drive neurons into response saturation, thus minimising neuronal variability despite noisy visual input signals.