Multiplicative computation in a visual neuron sensitive to looming

Gabbiani, Fabrizio; Krapp, Holger G.; Koch, Christof; Laurent, Gilles

doi:10.1038/nature01190

Cited by 370 publications

(342 citation statements)

References 29 publications

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“…The output of these two downstream neurons would then be integrated, for example through recurrent connectivity, intracellular molecules like calcium, or extracellular molecules like neuropeptides (Durstewitz et al, 2000;Flavell et al, 2013;Major and Tank, 2004). Finally, the two integrated values are combined by divisive inhibition to yield an output that is used to control female speed (Gabbiani et al, 2002;Silver, 2010). …”

Section: Linking Computations In the Auditory Pathway With Female Behmentioning

confidence: 99%

Connecting Neural Codes with Behavior in the Auditory System of Drosophila

Clemens¹,

Girardin²,

Coen³

et al. 2018

Neuron

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SummaryBrains are optimized for processing ethologically relevant sensory signals. However, few studies have characterized the neural coding mechanisms that underlie the transformation from natural sensory information to behavior. Here, we focus on acoustic communication in Drosophila melanogaster, and use computational modeling to link natural courtship song, neuronal codes, and female behavioral responses to song. We show that melanogaster females are sensitive to long timescale song structure (on the order of tens of seconds). From intracellular recordings, we generate models that recapitulate neural responses to acoustic stimuli. We link these neural codes with female behavior by generating model neural responses to natural courtship song. Using a simple decoder, we predict female behavioral responses to the same song stimuli with high accuracy. Our modeling approach reveals how long timescale song features are represented by the Drosophila brain, and how neural representations can be decoded to generate behavioral selectivity for acoustic communication signals.

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Section: Linking Computations In the Auditory Pathway With Female Behmentioning

confidence: 99%

Connecting Neural Codes with Behavior in the Auditory System of Drosophila

Clemens¹,

Girardin²,

Coen³

et al. 2018

Neuron

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“…The best studied collision avoidance system in insects is the one of the Locust, known as the Lobula Giant Movement Detector (LGMD), which relies exclusively on vision. The LGMD has been shown to robustly signal collisions with objects independent of their size, texture, shape and approaching angle (Gabbiani et al 1999;Gabbiani 2001;Gabbiani 2002;Gabbiani 2004). Previous studies shave shown that a system that extracts visual expansion, i.e.…”

Section: Collision Avoidancementioning

confidence: 99%

“…Many sensors, from ultrasonic, infrared and laser sensors to vision have been applied to this purpose, although no general solution has been found (Fox et al 1997;Surmann et al 2003;Harrison 2005). The neural correlate of collision avoidance has been identified in many insect species, and due to its impressive performance and accessibility it has been subject of abundant research (Rowel 1971;Gabbiani 2002;Tammero & Dickinson 2002;Krapp & Gabbiani 2005). The best studied collision avoidance system in insects is the one of the Locust, known as the Lobula Giant Movement Detector (LGMD), which relies exclusively on vision.…”

Section: Collision Avoidancementioning

confidence: 99%

Humanitarian Demining Using an Insect Based Chemical Unmanned Aerial Vehicle

Bermúdez¹

2008

Humanitarian Demining

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“…Experimental evidence for such multiplicative or modulatory interactions between neurons abounds. Gabbiani et al (2002), e.g., have verified that specific neurons of locusts perform a multiplication of two input signals. A very prominent example in higher animals are the dopaminergic modulations of cortical input to the striatum (e.g., Freund et al 1985, Nicola et al 2000.…”

Section: Physiological Background Of Dynamic Routingmentioning

confidence: 99%

Information Routing, Correspondence Finding, and Object Recognition in the Brain

Wolfrum¹

2010

Studies in Computational Intelligence

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This work would not have been possible without the people I have had the honor of working with over the past years. First of all I want to thank Christoph von der Malsburg, whose scientific concepts have inspired large parts of this thesis, and who granted me enough freedom to follow my own research directions while at the same time providing motivation whenever this was necessary. I also thank Rudolf Mester for good discussions that helped me retain my engineering viewpoint on the interdisciplinary problems that I worked on. Discussions with Geoff Goodhill, Bruno Olshausen, Jochen Triesch, and Alan Yuille, among others, have shaped my thinking and had an important impact on this thesis.I thank Urs Bergmann, Jenia Jitsev, and Junmei Zhu for proof-reading parts of the thesis, and Jörg Lücke for good collaboration. The neurogroups at FIAS have provided a rich and stimulating environment, it has been fun working and thinking with you! I also thank all colleagues at FIAS for good company and the truly interdisciplinary interaction we had (e.g. at Hirschegg). Last but not least I owe this thesis to my parents, who planted curiosity and a desire for understanding in me that were stronger than the difficulties I met during this dissertation. iv

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Multiplicative computation in a visual neuron sensitive to looming

Cited by 370 publications

References 29 publications

Connecting Neural Codes with Behavior in the Auditory System of Drosophila

Connecting Neural Codes with Behavior in the Auditory System of Drosophila

Humanitarian Demining Using an Insect Based Chemical Unmanned Aerial Vehicle

Information Routing, Correspondence Finding, and Object Recognition in the Brain

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