Microsaccadic sampling of moving image information provides Drosophila hyperacute vision

Juusola, Mikko; Dau, An; Song, Zhuoyi; Solanki, Narendra; Rien, Diana; Jaciuch, David; Dongre, Sidhartha A.; Blanchard, Florence; Polavieja, Gonzalo G. de; Hardie, Roger C.; Takalo, Jouni

doi:10.7554/elife.26117

Cited by 60 publications

(132 citation statements)

References 201 publications

(782 reference statements)

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“…To confirm that flies are capable of perceiving such small objects on our LED display, we used a systems identification method during tethered flight [26] to show robust steering responses to either a single pixel object or two-pixel object displaced in single-pixel increments (Figure 1M inset). In principle, any photoreceptor could respond to the luminance decrement generated by an object spanning less than its total acceptance angle, yet it is remarkable that simple hyperacuity has been documented within lobula neurons of hoverflies [3] and more recently within Drosophila photoreceptors [27]. …”

Section: Resultsmentioning

confidence: 99%

Object-Detecting Neurons in Drosophila

2017

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SUMMARY Many animals rely on vision to detect objects such as conspecifics, predators, and prey. Hypercomplex cells found in feline cortex and small target motion detectors found in dragonfly and hoverfly optic lobes demonstrate robust tuning for small objects with weak or no response to larger objects or movement of the visual panorama [1–3]. However, the relationship between anatomical, molecular, and functional properties of object detection circuitry is not understood. Here, we characterize a specialized object detector in Drosophila, the lobula columnar neuron LC11 [4]. By imaging calcium dynamics with two-photon excitation microscopy we show that LC11 responds to the omni-directional movement of a small object darker than the background, with little or no responses to static flicker, vertically elongated bars, or panoramic gratings. LC11 dendrites innervate multiple layers of the lobula, and each dendrite spans enough columns to sample 75-degrees of visual space, yet the area that evokes calcium responses is only 20-degrees wide, and shows robust responses to a 2.2-degree object spanning less than half of one facet of the compound eye. The dendrites of neighboring LC11s encode object motion retinotopically, but the axon terminals fuse into a glomerular structure in the central brain where retinotopy is lost. Blocking inhibitory ionic currents abolishes small object sensitivity and facilitates responses to elongated bars and gratings. Our results reveal high acuity object motion detection in the Drosophila optic lobe.

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Section: Resultsmentioning

confidence: 99%

Object-Detecting Neurons in Drosophila

2017

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“…While the description given is accurate, the reality is more complex and in perpetual motion, and we know that optimal visual information sampling, at least in Drosophila , further involves elaborate photomechanical adaptations and self‐motion (body, head and eye movements), which prevent retinal images from fading during fast adaptation (Juusola et al . ). In fact, during photon sampling, light input intensity is regulated by two photomechanical processes inside photoreceptors.…”

Section: Discussionmentioning

confidence: 97%

“…; Juusola et al . ). As the output frequency distribution flattens (or whitens) while its amplitude distribution becomes Gaussian, every symbol (voltage value) of a message (macroscopic voltage response) would be transmitted equally often (Shannon, ).…”

Section: Introduction To Adaptive Quantal Information Samplingmentioning

confidence: 97%

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How a fly photoreceptor samples light information in time

Juusola

Song

2017

The Journal of Physiology

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A photoreceptor's information capture is constrained by the structure and function of its light‐sensitive parts. Specifically, in a fly photoreceptor, this limit is set by the number of its photon sampling units (microvilli), constituting its light sensor (the rhabdomere), and the speed and recoverability of their phototransduction reactions. In this review, using an insightful constructionist viewpoint of a fly photoreceptor being an ‘imperfect’ photon counting machine, we explain how these constraints give rise to adaptive quantal information sampling in time, which maximises information in responses to salient light changes while antialiasing visual signals. Interestingly, such sampling innately determines also why photoreceptors extract more information, and more economically, from naturalistic light contrast changes than Gaussian white‐noise stimuli, and we explicate why this is so. Our main message is that stochasticity in quantal information sampling is less noise and more processing, representing an ‘evolutionary adaptation’ to generate a reliable neural estimate of the variable world.

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“…, , ; Song & Juusola, ; Juusola et al . , ). This would be impossible with the conventional reductionist modelling approach.…”

Section: Introductionmentioning

confidence: 99%

A biomimetic fly photoreceptor model elucidates how stochastic adaptive quantal sampling provides a large dynamic range

Song

Juusola

2017

The Journal of Physiology

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Light intensities (photons s–1 μm–2) in a natural scene vary over several orders of magnitude from shady woods to direct sunlight. A major challenge facing the visual system is how to map such a large dynamic input range into its limited output range, so that a signal is neither buried in noise in darkness nor saturated in brightness. A fly photoreceptor has achieved such a large dynamic range; it can encode intensity changes from single to billions of photons, outperforming man‐made light sensors. This performance requires powerful light adaptation, the neural implementation of which has only become clear recently. A computational fly photoreceptor model, which mimics the real phototransduction processes, has elucidated how light adaptation happens dynamically through stochastic adaptive quantal information sampling. A Drosophila R1–R6 photoreceptor's light sensor, the rhabdomere, has 30,000 microvilli, each of which stochastically samples incoming photons. Each microvillus employs a full G‐protein‐coupled receptor signalling pathway to adaptively transduce photons into quantum bumps (QBs, or samples). QBs then sum the macroscopic photoreceptor responses, governed by four quantal sampling factors (limitations): (i) the number of photon sampling units in the cell structure (microvilli), (ii) sample size (QB waveform), (iii) latency distribution (time delay between photon arrival and emergence of a QB), and (iv) refractory period distribution (time for a microvillus to recover after a QB). Here, we review how these factors jointly orchestrate light adaptation over a large dynamic range.

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Microsaccadic sampling of moving image information provides Drosophila hyperacute vision

Cited by 60 publications

References 201 publications

Object-Detecting Neurons in Drosophila

Object-Detecting Neurons in Drosophila

How a fly photoreceptor samples light information in time

A biomimetic fly photoreceptor model elucidates how stochastic adaptive quantal sampling provides a large dynamic range

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