Image statistics of the environment surrounding freely behaving hoverflies

Dyakova, Olga; Müller, Martin; Egelhaaf, Martin; Nordström, Karin

doi:10.1007/s00359-019-01329-1

Cited by 6 publications

(7 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By projecting the individual features in the sparse dot field onto a screen anterior of the hoverfly, their spatial location, and simulated z-depth, over time, provided the type of optic flow that would be generated by self-motion (24). In optic flow-sensitive descending neurons, this stimulus elicits strong direction-selective responses, similar to the responses to widefield sinusoidal gratings (24) or large moving images (22) with naturalistic statistics (32). We recorded from TSDNs (Fig.…”

Section: Resultsmentioning

confidence: 99%

Facilitation of neural responses to targets moving against optic flow

Nicholas

Nordström

2021

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

For the human observer, it can be difficult to follow the motion of small objects, especially when they move against background clutter. In contrast, insects efficiently do this, as evidenced by their ability to capture prey, pursue conspecifics, or defend territories, even in highly textured surrounds. We here recorded from target selective descending neurons (TSDNs), which likely subserve these impressive behaviors. To simulate the type of optic flow that would be generated by the pursuer’s own movements through the world, we used the motion of a perspective corrected sparse dot field. We show that hoverfly TSDN responses to target motion are suppressed when such optic flow moves syn-directional to the target. Indeed, neural responses are strongly suppressed when targets move over either translational sideslip or rotational yaw. More strikingly, we show that TSDNs are facilitated by optic flow moving counterdirectional to the target, if the target moves horizontally. Furthermore, we show that a small, frontal spatial window of optic flow is enough to fully facilitate or suppress TSDN responses to target motion. We argue that such TSDN response facilitation could be beneficial in modulating corrective turns during target pursuit.

show abstract

Section: Resultsmentioning

confidence: 99%

Facilitation of neural responses to targets moving against optic flow

Nicholas

Nordström

2021

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…By projecting the individual features in the sparse dot field onto a screen anterior of the hoverfly, their spatial location, and simulated z-depth, over time, provided the type of optic flow that would be generated by self-motion (24). In optic flow sensitive descending neurons, this stimulus elicits strong direction-selective responses, similar to the responses to widefield sinusoidal gratings (24), or large moving images (22) with naturalistic statistics (32). We confirmed that our optic flow stimulus did not generate a response in target selective descending neurons (TSDNs) in male Eristalis tenax hoverflies (Fig.…”

Section: Resultsmentioning

confidence: 96%

Facilitation of neural responses to targets moving against optic flow

Nicholas

Nordström

2020

Preprint

Self Cite

View full text Add to dashboard Cite

AbstractFor the human observer, it is difficult to follow the motion of small objects, especially when these are displayed against background motion. However, insects efficiently do this, as evidenced by their ability to capture prey, pursue conspecifics or defend territories, often in foliage-rich areas. This ability has been attributed to insect optic lobe neurons sharply tuned to target motion, as these respond robustly to targets even against moving backgrounds. However, their presumed post-synaptic counterparts, the target selective descending neurons (TSDNs), do not, suggesting inhibition by widefield motion. Importantly, though, previous experiments used panoramic background images, which not only lacked 3D cues, but also high-contrast features, experienced in natural flight. To mitigate this, we here use widefield motion consisting of many targets moving coherently to simulate the type of 3D optic flow that would be generated by the animal’s own motion through the world. We show that TSDNs are inhibited by syn-directional optic flow, consistent with previous findings. However, and more surprisingly, we show that TSDNs are facilitated by optic flow in the opposite direction to target motion. Our results thereby emphasize that the responses of descending neurons cannot simply be inferred from their presumed pre-synaptic counterparts.

show abstract

“…It has been hypothesised that visual sensory neurons must efficiently adapt to the regularities of the natural scene to take advantage of this spatio-temporal structure, and to efficiently code the information in the visual environment (Barlow, 1961). Hence, Insects visual neurons have evolved to provide robust and efficient responses to naturalistic inputs, enabling survival in complex ecological niches (Dyakova et al, 2019(Dyakova et al, , 2015Dyakova and Nordström, 2017). Numerous studies have highlighted the remarkable adaptability of the sensory pathway in insects, demonstrating robust response and behaviour across various input parameters, such as contrast, spatial frequency and spatiotemporal correlations (Arenz et al, 2017;Brinkworth and O'Carroll, 2009;Clark et al, 2014;Dyakova et al, 2019;Dyakova and Nordström, 2017;Schwegmann et al, 2014;Serbe et al, 2016;Juusola, 2014, 2014;Van Hateren, 1997;van Hateren, 1992).…”

Section: Introductionmentioning

confidence: 99%

A neuromorphic model of active vision shows spatio-temporal encoding in lobula neurons can aid pattern recognition in bees

MaBouDi

Roper

Guiraud

et al. 2023

Preprint

View full text Add to dashboard Cite

Bees possess remarkable cognitive abilities in on-the-fly visual learning, making them an ideal model for studying active information acquisition and representation. In this study, we investigated the minimal circuitry required for active vision in bees by considering their flight behaviours during visual pattern scanning. By developing a neural network model inspired by the insect visual system, we examined the influence of scanning behaviour on optic lobe connectivity and neural activity. Through the incorporation of non-associative learning and exposure to diverse natural images, we obtained compelling results that align with neurobiological observations. Our findings reveal that active scanning and non-associative learning dynamically shape the connectivity within the visual lobe, resulting in an efficient representation of visual input. Interestingly, we observed self-organization in orientation-selective neurons in the lobula region, characterized by sparse responses to orthogonal bar movements. These dynamic orientation-selective cells cover various orientations, exhibiting a bias towards the speed and contrast of input sampling. To assess the effectiveness of this spatiotemporal coding for pattern recognition, we integrated our model with the mushroom body circuitry underlying associative learning. Notably, our model demonstrated impressive performance across several pattern recognition tasks, suggesting a similar coding system within the bee visual system. Overall, this study integrates behavioural experiments, neurobiological findings, and computational models to reveal how complex visual features can be condensed through spatiotemporal encoding in the lobula neurons, facilitating efficient sampling of visual cues for identifying rewarding foraging resources. Our findings have broader implications for understanding active vision in diverse animals, including humans, and offer valuable insights for the application of bio-inspired principles in the design of autonomous robots.

show abstract

Image statistics of the environment surrounding freely behaving hoverflies

Cited by 6 publications

References 76 publications

Facilitation of neural responses to targets moving against optic flow

Facilitation of neural responses to targets moving against optic flow

Facilitation of neural responses to targets moving against optic flow

A neuromorphic model of active vision shows spatio-temporal encoding in lobula neurons can aid pattern recognition in bees

Contact Info

Product

Resources

About