Microsaccadic sampling of moving image information provides<i>Drosophila</i>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; Takalo, Jouni

doi:10.1101/083691

Cited by 12 publications

(186 citation statements)

References 200 publications

(427 reference statements)

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“…The connectomic and anatomical data currently in the FFBO platform includes all available open fly brain data from the (i) FlyCircuit [20], spanning some 20,000 neurons and 1,260,000 inferred synaptic connections [25], and (ii) the Janelia seven column Medulla EM reconstruction that includes some 500 neurons and 67,000 synapses [15,42], and (iii) the Janelia larval EM reconstruction that currently includes some 500 neurons and 138,000 synapses [43,44]. The physiological data currently in the database consists of 1.6 hours of electrophysiology recordings from photoreceptors [29,45], olfactory sensory neurons [46] and antennal lobe projection neurons [47].…”

Section: The Fruit Fly Brain Observatorymentioning

confidence: 99%

The Fruit Fly Brain Observatory: From Structure to Function

Nh¹,

C²,

Tomkins

et al. 2019

Preprint

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The fruit fly is a key model organism for studying the activity of interconnected brain circuits. A large scattered global research community of neurobiologists and neurogeneticists, computational and theoretical neuroscientists, and computer scientists and engineers has been developing a vast trove of experimental and modeling data that has yet to be distilled into new knowledge and understanding of the functional logic of the brain. Developing open shared models, modelling tools and data repositories that can be accessed from anywhere in the world is the necessary engine for accelerating our understanding of how the brain works.To that end we developed the Fruit Fly Brain Observatory (FFBO), the next generation open-source platform to support open, collaborative Drosophila neuroscience research. FFBO provides a (i) hub for storing and integrating fruit fly brain research data from multiple data sources worldwide, (ii) unified repository of tools and methods to build, emulate and compare fruit fly brain models in health and disease, and (iii) an open framework for fruit fly brain data processing and model execution. FFBO provides access to application tools for visualizing, configuring, simulating and analyzing computational models of brain circuits of the (i) cell type map, (ii) connectome, (iii) synaptome, and (iv) activity map using intuitive queries in plain English. Tools are provided to extract the function inherent in these structural maps. All applications can be accessed with any modern browser.

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Section: The Fruit Fly Brain Observatorymentioning

confidence: 99%

The Fruit Fly Brain Observatory: From Structure to Function

Nh¹,

C²,

Tomkins

et al. 2019

Preprint

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“…[4] for a viewing distance of˜3 body lengths using the inter-ommatidial angle of 4.8° [5]. C: The same image and distance, but using a conservative estimate of the effective acuity determined by Juusola et al [6] of˜1.5°.…”

Section: Introductionmentioning

confidence: 99%

“…However, recent physiological experiments [6] reveal that D. melanogaster can 11 respond to details as fine as 1.16°as long as they are presented (to a tethered fly) at 12 specific speeds. These speeds happen to coincide with D. melanogaster 's natural 13 saccadic gait [7], which strongly suggests that naturally behaving D. melanogaster have 14 much finer than the inter-ommatidial angle of 4.8° [5].…”

Section: Introductionmentioning

confidence: 99%

“…Combined, these results open up the possibility that 26 Drosophila uses vision to a much greater extent in object recognition, perhaps even 27 using it to discriminate between species or sex (supplementing other olfactory cues that 28 are known to convey this information [12]). 29 Even with D. melanogaster 's photoreceptor hyper-acuity [6], the image that is 30 received is only around 29×29 units (or pixels; Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

“…We show here that each D. melanogaster 203 has visually distinguishable features that persist across days. This fact, combined with 204 their hyper acuity [6] and theoretical capacity of their visual network, provides a proof 205 of principle against the traditional belief that Drosophila only see blurry motion. In Supporting information 209 S1 Fig.…”

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Can Drosophila melanogaster tell who’s who?

Schneider

Murali

Taylor

et al. 2018

Preprint

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Drosophila melanogaster are known to live in a social but cryptic world of touch and odours, but the extent to which they can perceive and integrate visual information is a hotly debated topic. Some researchers fixate on the limited resolution of D. melanogaster 's optics, other's on their seemingly identical appearance; yet there is evidence of individual recognition and surprising visual learning in flies. Here, we apply machine learning and show that individual D. melanogaster are visually distinct. We also use the striking similarity of Drosophila's visual system to current convolutional neural networks to theoretically investigate D. melanogaster 's capacity for visual understanding. We find that, despite their limited optical resolution, D. melanogaster 's neuronal architecture has the capability to extract and encode a rich feature set that allows flies to re-identify individual conspecifics with surprising accuracy. These experiments provide a proof of principle that Drosophila inhabit in a much more complex visual world than previously appreciated. Author summaryIn this paper, we determine a proof of principle for inter-individual recognition in two parts; is there enough information contained in low resolution pictures for inter-fly discrimination, and if so does Drosophila's visual system have enough capacity to use it. We show that the information contained in a 29×29 pixel image (number of ommatidia in a fly eye) is sufficient to achieve 94% accuracy in fly re-identification. Further, we show that the fly eye has the theoretical capacity to identify another fly with about 75% accuracy. Although it is unlikely that flies use the exact algorithm we tested, our results show that, in principle, flies may be using visual perception in ways that are not usually appreciated. 6 only˜850 lens units (ommatidia), each capturing a single point in space, Drosophila's 7 June 5, 2018 1/12 40 non-familiar conspecifics in social situations [3]. 41 How a fly's visual system could extract meaning out of low resolution images is 42 suggested by the highly structured and layered organization of Drosophila's visual 43 system (Fig. 2C). At the input the ommatidia are packed one by one, but their 44 individually-tuned photoreceptors are arranged spatially to essentially convolve a 6-unit 45 filter across the receptive field. The output of this photoreceptor filter is, in turn, the 46 input for downstream medulla neurons that connect to several 'columns' of 47 photoreceptor outputs. This filter-convolution and using the output of one filter as a 48 'feature map' for another layer is a hallmark of the engineered architectures of DCNs 49that dominate computer vision today (one such DCN is illustrated in Fig. 2A). Just as 50 DCNs can take low level image representations and encode them into a semantic 51 June 5, 2018 2/12 71 therefore we were specifically limited to 'modular' neurons (with 1 neuron/column) 72 throughout the medulla [17]. The connections between neuronal types was extracted 73 from published connectomes [18]. We...

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