@article {69881, title = {Comparative connectomics of Drosophila descending and ascending neurons.}, journal = {Nature}, year = {2025}, month = {2025 Apr 30}, abstract = {

In most complex nervous systems there is a clear anatomical separation between the nerve cord, which contains most of the final motor outputs necessary for behaviour, and the brain. In insects, the neck connective is both a physical and an information bottleneck connecting the brain and the ventral nerve cord (an analogue of the spinal cord) and comprises diverse populations of descending neurons (DNs), ascending neurons (ANs) and sensory ascending neurons, which are crucial for sensorimotor signalling and control. Here, by integrating three separate electron microscopy (EM) datasets, we provide a complete connectomic description of the ANs and DNs of the Drosophila female nervous system and compare them with neurons of the male nerve cord. Proofread neuronal reconstructions are matched across hemispheres, datasets and sexes. Crucially, we also match 51\% of DN cell types to light-level data defining specific driver lines, as well as classifying all ascending populations. We use these results to reveal the anatomical and circuit logic of neck connective neurons. We observe connected chains of DNs and ANs spanning the neck, which may subserve motor sequences. We provide a complete description of sexually dimorphic DN and AN populations, with detailed analyses of selected circuits for reproductive behaviours, including male courtship (DNa12; also known as aSP22) and song production (AN neurons from hemilineage 08B) and female ovipositor extrusion (DNp13). Our work provides EM-level circuit analyses that span the entire central nervous system of an adult animal.

}, issn = {1476-4687}, doi = {10.1038/s41586-025-08925-z}, url = {https://doi.org/10.1038/s41586-025-08925-z}, author = {St{\"u}rner, Tomke and Brooks, Paul and Serratosa Capdevila, Laia and Morris, Billy J and Javier, Alexandre and Fang, Siqi and Gkantia, Marina and Cachero, Sebastian and Beckett, Isabella R and Marin, Elizabeth C and Schlegel, Philipp and Champion, Andrew S and Moitra, Ilina and Richards, Alana and Klemm, Finja and Kugel, Leonie and Namiki, Shigehiro and Cheong, Han S J and Kovalyak, Julie and Tenshaw, Emily and Parekh, Ruchi and Phelps, Jasper S and Mark, Brandon and Dorkenwald, Sven and Bates, Alexander S and Matsliah, Arie and Yu, Szi-Chieh and McKellar, Claire E and Sterling, Amy and Seung, H Sebastian and Murthy, Mala and Tuthill, John C and Lee, Wei-Chung Allen and Card, Gwyneth M and Costa, Marta and Jefferis, Gregory S X E and Eichler, Katharina} } @article {70155, title = {Sexual dimorphism in the complete connectome of the Drosophila male central nervous system}, journal = {bioRxiv}, year = {2025}, month = {2025 Oct 09}, abstract = {

Sex differences in behaviour exist across the animal kingdom, typically under strong genetic regulation. In Drosophila, previous work has shown that fruitless and doublesex transcription factors identify neurons driving sexually dimorphic behaviour. However, the organisation of dimorphic neurons into functional circuits remains unclear.We now present the connectome of the entire Drosophila male central nervous system. This contains 166,691 neurons spanning the brain and ventral nerve cord, fully proofread and comprehensively annotated including fruitless and doublesex expression and 11,691 cell types. By comparison with a previous female brain connectome, we provide the first comprehensive description of the differences between male and female brains to synaptic resolution. Of 7,319 cross-matched cell types in the central brain, 114 are dimorphic with an additional 262 male- and 69 female-specific (totalling 4.8\% of neurons in males and 2.4\% in females).This resource enables analysis of full sensory-to-motor circuits underlying complex behaviours as well as the impact of dimorphic elements. Sex-specific and dimorphic neurons are concentrated in higher brain centres while the sensory and motor periphery are largely isomorphic. Within higher centres, male-specific connections are organised into hotspots defined by male-specific neurons or the presence of male-specific arbours on neurons that are otherwise similar between sexes. Numerous circuit switches reroute sensory information to form conserved, antagonistic circuits controlling opposing behaviours.

}, doi = {10.1101/2025.10.09.680999}, url = {https://www.biorxiv.org/content/early/2025/10/09/2025.10.09.680999}, author = {Berg, Stuart and Beckett, Isabella R and Costa, Marta and Schlegel, Philipp and Januszewski, Micha{\l} and Marin, Elizabeth C and Nern, Aljoscha and Preibisch, Stephan and Qiu, Wei and Takemura, Shin-ya and Fragniere, Alexandra MC and Champion, Andrew S and Adjavon, Diane-Yayra and Cook, Michael and Gkantia, Marina and Hayworth, Kenneth J and Huang, Gary B and Katz, William T and K{\"a}mpf, Florian and Lu, Zhiyuan and Ordish, Christopher and Paterson, Tyler and St{\"u}rner, Tomke and Trautman, Eric T and Whittle, Catherine R and Burnett, Laura E and Hoeller, Judith and Li, Feng and Loesche, Frank and Morris, Billy J and Pietzsch, Tobias and Pleijzier, Markus W and Silva, Valeria and Yin, Yijie and Ali, Iris and Badalamente, Griffin and Bates, Alexander Shakeel and Bogovic, John and Brooks, Paul and Cachero, Sebastian and Canino, Brandon S and Chaisrisawatsuk, Bhumpanya and Clements, Jody and Crowe, Arthur and de Haan Vicente, In{\^e}s and Dempsey, Georgia and Don{\`a}, Erika and dos Santos, M{\'a}rcia and Dreher, Marisa and Dunne, Christopher R and Eichler, Katharina and Finley-May, Samantha and Flynn, Miriam A and Hameed, Imran and Hopkins, Gary Patrick and Hubbard, Philip M and Kiassat, Ladann and Kovalyak, Julie and Lauchie, Shirley A and Leonard, Meghan and Lohff, Alanna and Longden, Kit D and Maldonado, Charli A and Mitletton, Myrto and Moitra, Ilina and Moon, Sung Soo and Mooney, Caroline and Munnelly, Eva J and Okeoma, Nneoma and Olbris, Donald J and Pai, Anika and Patel, Birava and Phillips, Emily M and Plaza, Stephen M and Richards, Alana and Rivas Salinas, Jennifer and Roberts, Ruair{\'\i} Jv and Rogers, Edward M and Scott, Ashley L and Scuderi, Louis A and Seenivasan, Pavithraa and Serratosa Capdevila, Laia and Smith, Claire and Svirskas, Rob and Takemura, Satoko and Tastekin, Ibrahim and Thomson, Alexander and Umayam, Lowell and Walsh, John J and Whittome, Holly and Xu, C Shan and Yakal, Emily A and Yang, Tansy and Zhao, Arthur and George, Reed and Jain, Viren and Jayaraman, Vivek and Korff, Wyatt and Meissner, Geoffrey W and Romani, Sandro and Funke, Jan and Knecht, Christopher and Saalfeld, Stephan and Scheffer, Louis K and Waddell, Scott and Card, Gwyneth M and Ribeiro, Carlos and Reiser, Michael B and Hess, Harald F and Rubin, Gerald M and Jefferis, Gregory Sxe} } @article {69721, title = {A split-GAL4 driver line resource for Drosophila neuron types}, year = {2025}, month = {01/2025}, doi = {10.7554/elife.98405.3}, url = {http://dx.doi.org/10.7554/eLife.98405.3}, author = {Meissner, Geoffrey W and Vannan, Allison and Jeter, Jennifer and Close, Kari and Depasquale, Gina M and Dorman, Zachary and Forster, Kaitlyn and Beringer, Jaye Anne and Gibney, Theresa V and Hausenfluck, Joanna H and He, Yisheng and Henderson, Kristin and Johnson, Lauren and Johnston, Rebecca M and Ihrke, Gudrun and Iyer, Nirmala and Lazarus, Rachel and Lee, Kelley and Li, Hsing-Hsi and Liaw, Hua-Peng and Melton, Brian and Miller, Scott and Motaher, Reeham and Novak, Alexandra and Ogundeyi, Omotara and Petruncio, Alyson and Price, Jacquelyn and Protopapas, Sophia and Tae, Susana and Taylor, Jennifer and Vorimo, Rebecca and Yarbrough, Brianna and Zeng, Kevin Xiankun and Zugates, Christopher T and Dionne, Heather and Angstadt, Claire and Ashley, Kelly and Cavallaro, Amanda and Dang, Tam and Gonzalez, Guillermo A and Hibbard, Karen L and Huang, Cuizhen and Kao, Jui-Chun and Laverty, Todd and Mercer, Monti and Perez, Brenda and Pitts, Scarlett and Ruiz, Danielle and Vallanadu, Viruthika and Zheng, Grace Zhiyu and Goina, Cristian and Otsuna, Hideo and Rokicki, Konrad and Svirskas, Robert R and Cheong, Han Sj and Dolan, Michael-John and Ehrhardt, Erica and Feng, Kai and El Galfi, Basel and Goldammer, Jens and Huston, Stephen J and Hu, Nan and Ito, Masayoshi and McKellar, Claire and minegishi, ryo and Namiki, Shigehiro and Nern, Aljoscha and Schretter, Catherine E and Sterne, Gabriella R and Venkatasubramanian, Lalanti and Wang, Kaiyu and Wolff, Tanya and Wu, Ming and George, Reed and Malkesman, Oz and Aso, Yoshinori and Card, Gwyneth M and Dickson, Barry J and Korff, Wyatt and Ito, Kei and Truman, James W and Zlatic, Marta and Rubin, Gerald M} } @article {70030, title = {Transforming descending input into motor output: An analysis of the Drosophila Male Adult Nerve Cord connectome}, journal = {eLife}, year = {2025}, month = {2025 Jul 21}, abstract = {

In most animals, a relatively small number of descending neurons (DNs) connect higher brain centers in the animal{\textquoteright}s head to circuits and motor neurons (MNs) in the nerve cord of the animal{\textquoteright}s body that effect movement of the limbs. To understand how brain signals generate behavior, it is critical to understand how these descending pathways are organized onto the body MNs. In the fly,\ Drosophila melanogaster, MNs controlling muscles in the leg, wing, and other motor systems reside in a ventral nerve cord (VNC), analogous to the mammalian spinal cord. In companion papers, we introduced a densely-reconstructed connectome of the\ Drosophila\ Male Adult Nerve Cord (MANC, (Takemura et al., 2024)), including cell type and developmental lineage annotation (Marin et al., 2024), which provides complete VNC connectivity at synaptic resolution. Here, we present a first look at the organization of the VNC networks connecting DNs to MNs based on this new connectome information. We proofread and curated all DNs and MNs to ensure accuracy and reliability, then systematically matched DN axon terminals and MN dendrites with light microscopy data to link their VNC morphology with their brain inputs or muscle targets. We report both broad organizational patterns of the entire network and fine-scale analysis of selected circuits of interest. We discover that direct DN-MN connections are infrequent and identify communities of intrinsic neurons linked to control of different motor systems, including putative ventral circuits for walking, dorsal circuits for flight steering and power generation, and intermediate circuits in the lower tectulum for coordinated action of wings and legs. Our analysis generates hypotheses for future functional experiments and, together with the MANC connectome, empowers others to investigate these and other circuits of the\ Drosophila\ ventral nerve cord in richer mechanistic detail.

}, doi = {10.7554/elife.96084.2}, url = {http://dx.doi.org/10.7554/eLife.96084.2}, author = {Cheong, Han Sj and Eichler, Katharina and St{\"u}rner, Tomke and Asinof, Samuel K and Champion, Andrew S and Marin, Elizabeth C and Oram, Tess B and Sumathipala, Marissa and Venkatasubramanian, Lalanti and Namiki, Shigehiro and Siwanowicz, Igor and Costa, Marta and Berg, Stuart and Jefferis, Gregory Sxe and Card, Gwyneth M} } @article {70123, title = {An updated catalogue of split-GAL4 driver lines for descending neurons in Drosophila melanogaster}, journal = {eLife}, year = {2025}, month = {2025 Sept 19}, abstract = {

Descending neurons (DNs) occupy a key position in the sensorimotor hierarchy, conveying signals from the brain to the rest of the body below the neck. In\ Drosophila melanogaster\ flies, approximately 480 DN cell types have been described from electron-microscopy image datasets. Genetic access to these cell types is crucial for further investigation of their role in generating behaviour. We previously conducted the first large-scale survey of\ Drosophila melanogaster\ DNs, describing 98 unique cell types from light microscopy and generating cell-type-specific split-Gal4 driver lines for 65 of them. Here, we extend our previous work, describing the morphology of 146 additional DN types from light microscopy, bringing the total number DN types identified in light microscopy datasets to 244, or roughly 50\% of all DN types. In addition, we produced 500 new sparse split-Gal4 driver lines and compiled a list of previously published DN lines from the literature for a combined list of 806 split-Gal4 driver lines targeting 190 DN types.

}, doi = {10.7554/elife.107450.1}, url = {http://dx.doi.org/10.7554/eLife.107450.1}, author = {Zung, Jessica L and Namiki, Shigehiro and Meissner, Geoffrey W and Cheong, Han Sj and Costa, Marta and Eichler, Katharina and St{\"u}rner, Tomke and Jefferis, Gregory Sxe and Managan, Claire and Korff, Wyatt and Card, Gwyneth M} } @article {69438, title = {Neural Control of Naturalistic Behavior Choices}, journal = {Annu Rev Neurosci}, year = {2024}, month = {2024 May 09}, abstract = {

In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology. In this approach, experimenters select animal models based on experimental accessibility and then utilize cutting-edge tools such as connectomes and genetically encoded reagents to analyze the flow of information through an animal{\textquoteright}s nervous system during naturalistic choice behaviors. We describe how the reverse neuroethology strategy has been applied to understand the neural underpinnings of innate, rapid decision making, with a focus on defensive behavioral choices in the vinegar fly Drosophila melanogaster.

}, issn = {1545-4126}, doi = {10.1146/annurev-neuro-111020-094019}, author = {Asinof, Samuel K and Card, Gwyneth M} } @article {69229, title = {Organization of an ascending circuit that conveys flight motor state in Drosophila.}, journal = {Current Biology}, year = {2024}, month = {2024 Feb 13}, abstract = {

Natural behaviors are a coordinated symphony of motor acts that drive reafferent (self-induced) sensory activation. Individual sensors cannot disambiguate exafferent (externally induced) from reafferent sources. Nevertheless, animals readily differentiate between these sources of sensory signals to carry out adaptive behaviors through corollary discharge circuits (CDCs), which provide predictive motor signals from motor pathways to sensory processing and other motor pathways. Yet, how CDCs comprehensively integrate into the nervous system remains unexplored. Here, we use connectomics, neuroanatomical, physiological, and behavioral approaches to resolve the network architecture of two pairs of ascending histaminergic neurons (AHNs) in Drosophila, which function as a predictive CDC in other insects. Both AHN pairs receive input primarily from a partially overlapping population of descending neurons, especially from DNg02, which controls wing motor output. Using Ca imaging and behavioral recordings, we show that AHN activation is correlated to flight behavior and precedes wing motion. Optogenetic activation of DNg02 is sufficient to activate AHNs, indicating that AHNs are activated by descending commands in advance of behavior and not as a consequence of sensory input. Downstream, each AHN pair targets predominantly non-overlapping networks, including those that process visual, auditory, and mechanosensory information, as well as networks controlling wing, haltere, and leg sensorimotor control. These results support the conclusion that the AHNs provide a predictive motor signal about wing motor state to mostly non-overlapping sensory and motor networks. Future work will determine how AHN signaling is driven by other descending neurons and interpreted by AHN downstream targets to maintain adaptive sensorimotor performance.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2024.01.071}, author = {Cheong, Han S J and Boone, Kaitlyn N and Bennett, Marryn M and Salman, Farzaan and Ralston, Jacob D and Hatch, Kaleb and Allen, Raven F and Phelps, Alec M and Cook, Andrew P and Phelps, Jasper S and Erginkaya, Mert and Lee, Wei-Chung A and Card, Gwyneth M and Daly, Kevin C and Dacks, Andrew M} } @article {68629, title = {A searchable image resource of Drosophila GAL4-driver expression patterns with single neuron resolution.}, journal = {eLife}, volume = {12}, year = {2023}, month = {2023 Feb 23}, abstract = {

Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

}, issn = {2050-084X}, doi = {10.7554/eLife.80660}, author = {Meissner, Geoffrey Wilson and Nern, Aljoscha and Dorman, Zachary and Depasquale, Gina M and Forster, Kaitlyn and Gibney, Theresa and Hausenfluck, Joanna H and He, Yisheng and Iyer, Nirmala A and Jeter, Jennifer and Johnson, Lauren and Johnston, Rebecca M and Lee, Kelley and Melton, Brian and Yarbrough, Brianna and Zugates, Christopher T and Clements, Jody and Goina, Cristian and Otsuna, Hideo and Rokicki, Konrad and Svirskas, Robert R and Aso, Yoshinori and Card, Gwyneth M and Dickson, Barry J and Ehrhardt, Erica and Goldammer, Jens and Ito, Masayoshi and Kainmueller, Dagmar and Korff, Wyatt and Mais, Lisa and minegishi, ryo and Namiki, Shigehiro and Rubin, Gerald M and Sterne, Gabriella R and Wolff, Tanya and Malkesman, Oz} } @article {68566, title = {Synaptic gradients transform object location to action.}, journal = {Nature}, volume = {613}, year = {2023}, month = {2023 Jan 04}, pages = {534-42}, abstract = {

To survive, animals must convert sensory information into appropriate behaviours. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. Here we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs) into synaptic weight gradients of VPN outputs onto central brain neurons. We demonstrate how this gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly{\textquoteright}s directional escape. Specifically, we discover that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, we demonstrate graded responses along the dorsoventral axis. We show that this synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs.

}, issn = {1476-4687}, doi = {10.1038/s41586-022-05562-8}, author = {Dombrovski, Mark and Peek, Martin Y and Park, Jin-Yong and Vaccari, Andrea and Sumathipala, Marissa and Morrow, Carmen and Breads, Patrick and Zhao, Arthur and Kurmangaliyev, Yerbol Z and Sanfilippo, Piero and Rehan, Aadil and Polsky, Jason and Alghailani, Shada and Tenshaw, Emily and Namiki, Shigehiro and Zipursky, S Lawrence and Card, Gwyneth M} } @article {67960, title = {Context-dependent control of behavior in Drosophila.}, journal = {Current Opinion in Neurobiology}, volume = {73}, year = {2022}, month = {2022 Mar 11}, pages = {102523}, abstract = {

The representation of contextual information peripheral to a salient stimulus is central to an animal{\textquoteright}s ability to correctly interpret and flexibly respond to that stimulus. While the computations and circuits underlying the context-dependent modulation of stimulus-response pairings have typically been studied in vertebrates, the genetic tractability, numeric simplification, and well-characterized connectivity patterns of the Drosophila melanogaster brain have facilitated circuit-level insights into contextual processing. Recent studies in flies reveal the neuronal mechanisms that create flexible context-dependent behavioral responses to sensory events in conditions of predation threat, feeding regulation, and social interaction.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2022.02.003}, author = {Oram, Tess B and Card, Gwyneth M} } @article {67952, title = {A functionally ordered visual feature map in the Drosophila brain.}, journal = {Neuron}, volume = {110}, year = {2022}, month = {2022 May 18}, pages = {1700}, abstract = {

Topographic maps, the systematic spatial ordering of neurons by response tuning, are common across species. In Drosophila, the lobula columnar (LC) neuron types project from the optic lobe to the central brain, where each forms a glomerulus in a distinct position. However, the advantages of this glomerular arrangement are unclear. Here, we examine the functional and spatial relationships of 10 glomeruli using single-neuron calcium imaging. We discover novel detectors for objects smaller than the lens resolution (LC18) and for complex line motion (LC25). We find that glomeruli are spatially clustered by selectivity for looming versus drifting object motion and ordered by size tuning to form a topographic visual feature map. Furthermore, connectome analysis shows that downstream neurons integrate from sparse subsets of possible glomeruli combinations, which are biased for glomeruli encoding similar features. LC neurons are thus an explicit example of distinct feature detectors topographically organized to facilitate downstream circuit integration.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2022.02.013}, author = {Klapoetke, Nathan C and Nern, Aljoscha and Rogers, Edward M and Rubin, Gerald M and Reiser, Michael B and Card, Gwyneth M} } @article {67892, title = {A population of descending neurons that regulates the flight motor of Drosophila.}, journal = {Current Biology}, volume = {32}, year = {2022}, month = {2022 Mar 14}, pages = {1189-1196}, abstract = {

Similar to many insect species, Drosophila melanogaster is capable of maintaining a stable flight trajectory for periods lasting up to several hours. Because aerodynamic torque is roughly proportional to the fifth power of wing length, even small asymmetries in wing size require the maintenance of subtle bilateral differences in flapping motion to maintain a stable path. Flies can even fly straight after losing half of a wing, a feat they accomplish via very large, sustained kinematic changes to both the damaged and intact wings. Thus, the neural network responsible for stable flight must be capable of sustaining fine-scaled control over wing motion across a large dynamic range. In this study, we describe an unusual type of descending neuron (DNg02) that projects directly from visual output regions of the brain to the dorsal flight neuropil of the ventral nerve cord. Unlike many descending neurons, which exist as single bilateral pairs with unique morphology, there is a population of at least 15 DNg02 cell pairs with nearly identical shape. By optogenetically activating different numbers of DNg02 cells, we demonstrate that these neurons regulate wingbeat amplitude over a wide dynamic range via a population code. Using two-photon functional imaging, we show that DNg02 cells are responsive to visual motion during flight in a manner that would make them well suited to continuously regulate bilateral changes in wing kinematics. Collectively, we have identified a critical set of descending neurons that provides the sensitivity and dynamic range required for flight control.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2022.01.008}, author = {Namiki, Shigehiro and Ros, Ivo G and Morrow, Carmen and Rowell, William J and Card, Gwyneth M and Korff, Wyatt and Dickinson, Michael H} } @article {67751, title = {Functional architecture of neural circuits for leg proprioception in Drosophila.}, journal = {Current Biology}, volume = {31}, year = {2021}, month = {2021 Dec 06}, pages = {5163}, abstract = {

To effectively control their bodies, animals rely on feedback from proprioceptive mechanosensory neurons. In the Drosophila leg, different proprioceptor subtypes monitor joint position, movement direction, and vibration. Here, we investigate how these diverse sensory signals are integrated by central proprioceptive circuits. We find that signals for leg joint position and directional movement converge in second-order neurons, revealing pathways for local feedback control of leg posture. Distinct populations of second-order neurons integrate tibia vibration signals across pairs of legs, suggesting a role in detecting external substrate vibration. In each pathway, the flow of sensory information is dynamically gated and sculpted by inhibition. Overall, our results reveal parallel pathways for processing of internal and external mechanosensory signals, which we propose mediate feedback control of leg movement and vibration sensing, respectively. The existence of a functional connectivity map also provides a resource for interpreting connectomic reconstruction of neural circuits for leg proprioception.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2021.09.035}, author = {Chen, Chenghao and Agrawal, Sweta and Mark, Brandon and Mamiya, Akira and Sustar, Anne and Phelps, Jasper S and Lee, Wei-Chung Allen and Dickson, Barry J and Card, Gwyneth M and Tuthill, John C} } @article {65852, title = {Multi-regional circuits underlying visually guided decision-making in Drosophila.}, journal = {Current Opinion in Neurobiology}, volume = {65}, year = {2020}, month = {2020 Nov 17}, pages = {77-87}, abstract = {

Visually guided decision-making requires integration of information from distributed brain areas, necessitating a brain-wide approach to examine its neural mechanisms. New tools in Drosophila melanogaster enable circuits spanning the brain to be charted with single cell-type resolution. Here, we highlight recent advances uncovering the computations and circuits that transform and integrate visual information across the brain to make behavioral choices. Visual information flows from the optic lobes to three primary central brain regions: a sensorimotor mapping area and two {\textquoteright}higher{\textquoteright} centers for memory or spatial orientation. Rapid decision-making during predator evasion emerges from the spike timing dynamics in parallel sensorimotor cascades. Goal-directed decisions may occur through memory, navigation and valence processing in the central complex and mushroom bodies.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2020.10.010}, author = {Cheong, Han and Siwanowicz, Igor and Card, Gwyneth M} } @article {65907, title = {Multi-regional circuits underlying visually guided decision-making in Drosophila.}, journal = {Current Opinion in Neurobiology}, volume = {65}, year = {2020}, month = {2020 Dec 01}, pages = {77-87}, abstract = {

Visually guided decision-making requires integration of information from distributed brain areas, necessitating a brain-wide approach to examine its neural mechanisms. New tools in Drosophila melanogaster enable circuits spanning the brain to be charted with single cell-type resolution. Here, we highlight recent advances uncovering the computations and circuits that transform and integrate visual information across the brain to make behavioral choices. Visual information flows from the optic lobes to three primary central brain regions: a sensorimotor mapping area and two {\textquoteright}higher{\textquoteright} centers for memory or spatial orientation. Rapid decision-making during predator evasion emerges from the spike timing dynamics in parallel sensorimotor cascades. Goal-directed decisions may occur through memory, navigation and valence processing in the central complex and mushroom bodies.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2020.10.010}, author = {Cheong, Han Sj and Siwanowicz, Igor and Card, Gwyneth M} } @article {65375, title = {Unc-4 acts to promote neuronal identity and development of the take-off circuit in the Drosophila CNS.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Mar 27}, doi = {10.7554/eLife.55007}, url = {https://elifesciences.org/articles/55007}, author = {Lacin, Haluk and Williamson, W Ryan and Card, Gwyneth M and Skeath, James B and Truman, James W} } @article {64825, title = {Neural basis for looming size and velocity encoding in the Drosophila giant fiber escape pathway.}, journal = {Current Biology : CB}, volume = {29}, year = {2019}, month = {2019 Feb 28}, pages = {1073}, abstract = {

Identified neuron classes in vertebrate cortical [1-4] and subcortical [5-8] areas and invertebrate peripheral [9-11] and central [12-14] brain neuropils encode specific visual features of a panorama. How downstream neurons integrate these features to control vital behaviors, like escape, is unclear [15]. In Drosophila, the timing of a single spike in the giant fiber (GF) descending neuron [16-18] determines whether a fly uses a short or long takeoff when escaping a looming predator [13]. We previously proposed that GF spike timing results from summation of two visual features whose detection is highly conserved across animals [19]: an object{\textquoteright}s subtended angular size and its angular velocity [5-8, 11, 20, 21]. We attributed velocity encoding to input from lobula columnar type 4 (LC4) visual projection neurons, but the size-encoding\ source remained unknown. Here, we show that lobula plate/lobula columnar, type 2 (LPLC2) visual projection neurons anatomically specialized to detect looming [22] provide the entire GF size component. We find LPLC2 neurons to be necessary for GF-mediated escape and show that LPLC2 and LC4 synapse directly onto the GF via reconstruction in a fly brain electron microscopy (EM) volume [23]. LPLC2 silencing eliminates the size component of the GF looming response in patch-clamp recordings, leaving only the velocity component. A model summing a linear function of angular velocity (provided by LC4) and a Gaussian function of angular size (provided by LPLC2) replicates GF looming response dynamics and predicts the peak response time. We thus present an identified circuit in which information from looming feature-detecting neurons is combined by a common post-synaptic target to determine behavioral output.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2019.01.079}, author = {Ache, Jan M and Polsky, Jason and Alghailani, Shada and Parekh, Ruchi and Breads, Patrick and Peek, Martin Y and Bock, Davi D and von Reyn, Catherine R and Card, Gwyneth M} } @article {65050, title = {State-dependent decoupling of sensory and motor circuits underlies behavioral flexibility in Drosophila.}, journal = {Nature Neuroscience}, volume = {22}, year = {2019}, month = {2019 Jul 01}, pages = {1132-1139}, abstract = {

An approaching predator and self-motion toward an object can generate similar looming patterns on the retina, but these situations demand different rapid responses. How central circuits flexibly process visual cues to activate appropriate, fast motor pathways remains unclear. Here we identify two descending neuron (DN) types that control landing and contribute to visuomotor flexibility in Drosophila. For each, silencing impairs visually evoked landing, activation drives landing, and spike rate determines leg extension amplitude. Critically, visual responses of both DNs are severely attenuated during non-flight periods, effectively decoupling visual stimuli from the landing motor pathway when landing is inappropriate. The flight-dependence mechanism differs between DN types. Octopamine exposure mimics flight effects in one, whereas the other probably receives neuronal feedback from flight motor circuits. Thus, this sensorimotor flexibility arises from distinct mechanisms for gating action-specific descending pathways, such that sensory and motor networks are coupled or decoupled according to the behavioral state.

}, issn = {1546-1726}, doi = {10.1038/s41593-019-0413-4}, author = {Ache, Jan M and Namiki, Shigehiro and Lee, Allen and Branson, Kristin and Card, Gwyneth M} } @article {48976, title = {Optogenetic dissection of descending behavioral control in Drosophila.}, journal = {eLife}, year = {2018}, month = {2018}, pages = {e34275}, abstract = {

In most animals, the brain makes behavioral decisions that are transmitted by descending neurons to the nerve cord circuitry that produces behaviors. In insects, only a few descending neurons have been associated with specific behaviors. To explore how descending neurons control an insect{\textquoteright}s movements, we developed a novel method to systematically assay the behavioral effects of activating individual neurons on freely behaving terrestrial\ D. melanogaster. We calculated a two-dimensional representation of the entire behavior space explored by these flies and we associated descending neurons with specific behaviors by identifying regions of this space that were visited with increased frequency during optogenetic activation. Applying this approach across a large collection of descending neurons, we found that (1) activation of most of the descending neurons drove stereotyped behaviors, (2) in many cases multiple descending neurons activated similar behaviors, and (3) optogenetically-activated behaviors were often dependent on the behavioral state prior to activation.

}, doi = {10.7554/eLife.34275}, author = {Cande, Jessica and Namiki, Shigehiro and Qiu, Jirui and Korff, Wyatt and Card, Gwyneth M and Shaevitz, Joshua W and Stern, David L and Berman, Gordon J} } @article {49418, title = {Speed dependent descending control of freezing behavior in Drosophila melanogaster.}, journal = {Nature Communications}, volume = {9}, year = {2018}, month = {2018 Sep 12}, pages = {3697}, abstract = {

The most fundamental choice an animal has to make when it detects a threat is whether to freeze, reducing its chances of being noticed, or to flee to safety. Here we show that Drosophila melanogaster exposed to looming stimuli in a confined arena either freeze or flee. The probability of freezing versus fleeing is modulated by the fly{\textquoteright}s walking speed at the time of threat, demonstrating that freeze/flee decisions depend on behavioral state. We describe a pair of descending neurons crucially implicated in freezing. Genetic silencing of DNp09 descending neurons disrupts freezing yet does not prevent fleeing. Optogenetic activation of both DNp09 neurons induces running and freezing in a state-dependent manner. Our findings establish walking speed as a key factor in defensive response choices and reveal a pair of descending neurons as a critical component in the circuitry mediating selection and execution of freezing or fleeing behaviors.

}, issn = {2041-1723}, doi = {10.1038/s41467-018-05875-1}, author = {Zacarias, Ricardo and Namiki, Shigehiro and Card, Gwyneth M and Vasconcelos, Maria Lu{\'\i}sa and Moita, Marta A} } @article {49510, title = {Tools for rapid high-resolution behavioral phenotyping of automatically isolated Drosophila.}, journal = {Cell Reports}, volume = {25}, year = {2018}, month = {2018 Nov 06}, pages = {1636-1649.e5}, abstract = {

Sparse manipulation of neuron excitability during free behavior is critical for identifying neural substrates of behavior. Genetic tools for precise neuronal manipulation exist in the fruit fly, Drosophila melanogaster, but behavioral tools are still lacking to identify potentially subtle phenotypes only detectible using high-throughput and high spatiotemporal resolution. We developed three assay components that can be used modularly to study natural and optogenetically induced behaviors. FlyGate automatically releases flies one at a time into an assay. FlyDetect tracks flies in real time, is robust to severe occlusions, and can be used to track appendages, such as the head. GlobeDisplay is a spherical projection system covering the fly{\textquoteright}s visual receptive field with a single projector. We demonstrate the utility of these components in an integrated system, FlyPEZ, by comprehensively modeling the input-output function for directional looming-evoked escape takeoffs and describing a millisecond-timescale phenotype from genetic silencing of a single visual projection neuron type.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2018.10.048}, author = {Williamson, W Ryan and Peek, Martin Y and Breads, Patrick and Coop, Brian and Card, Gwyneth M} } @article {48600, title = {Feature integration drives probabilistic behavior in the Drosophila escape response.}, journal = {Neuron}, volume = {94}, year = {2017}, month = {2017 Jun 21}, pages = {1190-204}, abstract = {

Animals rely on dedicated sensory circuits to extract and encode environmental features. How individual neurons integrate and translate these features into behavioral responses remains a major question. Here, we identify a visual\ projection neuron\ type that conveys predator approach information to the\ Drosophila\ giant fiber (GF) escape circuit. Genetic removal of this input during looming stimuli reveals that it encodes angular expansion velocity, whereas other input cell type(s) encode angular size. Motor program selection and timing emerge from\ linear\ integration of these two features within the GF. Linear integration improves size detection invariance over prior models and appropriately biases motor selection to rapid,\ GF-mediated\ escapes during fast looms. Our findings suggest\ feature integration, and motor control may occur as simultaneous operations within the same neuron and establish the\ Drosophila\ escape circuit as a model system in which these computations may be further dissected at the circuit level.

}, doi = {10.1016/j.neuron.2017.05.036}, author = {von Reyn, Catherine R and Nern, Aljoscha and Williamson, W Ryan and Breads, Patrick and Wu, Ming and Namiki, Shigehiro and Card, Gwyneth M} } @article {48975, title = {The functional organization of descending sensory-motor pathways in Drosophila.}, journal = {eLife}, year = {2017}, month = {2018 Jun 26}, pages = {e34272}, abstract = {

In most animals, the brain controls the body via a set of descending neurons (DNs) that traverse the neck. DN activity activates, maintains or modulates locomotion and other behaviors. Individual DNs have been well-studied in species from insects to primates, but little is known about overall connectivity patterns across the DN population. We systematically investigated DN anatomy in\ Drosophila melanogaster\ and created over 100 transgenic lines targeting individual cell types. We identified roughly half of all\ Drosophila\ DNs and comprehensively map connectivity between sensory and motor neuropils in the brain and nerve cord, respectively. We find the nerve cord is a layered system of neuropils reflecting the fly{\textquoteright}s capability for two largely independent means of locomotion -- walking and flight -- using distinct sets of appendages. Our results reveal the basic functional map of descending pathways in flies and provide tools for systematic interrogation of neural circuits.

}, doi = {10.7554/eLife.34272}, author = {Namiki, Shigehiro and Dickinson, Michael H and Wong, Allan M and Korff, Wyatt and Card, Gwyneth M} } @article {48646, title = {Mapping the neural substrates of behavior.}, journal = {Cell}, volume = {170}, year = {2017}, month = {2017-07-13}, pages = {393-406}, abstract = {

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

{\textbullet}We developed machine-vision methods to broadly and precisely quantify fly behavior{\textbullet}We measured effects of activating 2,204 genetically targeted neuronal populations{\textbullet}We created whole-brain maps of neural substrates of locomotor and social behaviors{\textbullet}We created resources for exploring our results and enabling further investigation

Machine-vision analyses of large behavior and neuroanatomy data reveal whole-brain maps of regions associated with numerous complex behaviors.

}, doi = {10.1016/j.cell.2017.06.032}, url = {http://dx.doi.org/10.1016/j.cell.2017.06.032}, author = {Robie, Alice A and Hirokawa, Jonathan and Edwards, Austin W and Umayam, Lowell A and Lee, Allen and Phillips, Mary L and Card, Gwyneth M and Korff, Wyatt and Rubin, Gerald M and Simpson, Julie H and Reiser, Michael B and Branson, Kristin M} } @article {48657, title = {A systematic nomenclature for the Drosophila ventral nervous system.}, journal = {bioRxiv}, year = {2017}, month = {2017 Apr 26}, abstract = {

Insect nervous systems are proven and powerful model systems for neuroscience research with wide relevance in biology and medicine. However, descriptions of insect brains have suffered from a lack of a complete and uniform nomenclature. Recognising this problem the Insect Brain Name Working Group produced the first agreed hierarchical nomenclature system for the adult insect brain, using Drosophila melanogaster as the reference framework, with other insect taxa considered to ensure greater consistency and expandability (Ito et al., 2014). Ito et al. (2014) purposely focused on the gnathal regions that account for approximately 50\% of the adult CNS. We extend this nomenclature system to the sub-gnathal regions of the adult Drosophila nervous system to provide a nomenclature of the so-called ventral nervous system (VNS), which includes the thoracic and abdominal neuromeres that was not included in the original work and contains the neurons that play critical roles underpinning most fly behaviours.

}, doi = {10.1101/122952}, author = {Court, Robert Christopher and Armstrong, James Douglas and Borner, Jana and Card, Gwyneth M and Costa, Marta and Dickinson, Michael H and Duch, Carsten and Korff, Wyatt and Mann, Richard S and Merritt, David and Murphey, Rodney K and Namiki, Shigehiro and Seeds, Andrew M and Shepherd, David and Shirangi, Troy R and Simpson, Julie H and Truman, James W and Tuthill, John C and Williams, Darren W} } @article {48934, title = {Ultra-selective looming detection from radial motion opponency.}, journal = {Nature}, volume = {551}, year = {2017}, month = {2017 Nov 08}, pages = {237-241}, abstract = {

Nervous systems combine lower-level sensory signals to detect higher-order stimulus features critical to survival, such as the visual looming motion created by an imminent collision or approaching predator. Looming-sensitive neurons have been identified in diverse animal species. Different large-scale visual features such as looming often share local cues, which means loom-detecting neurons face the challenge of rejecting confounding stimuli. Here we report the discovery of an ultra-selective looming detecting neuron, lobula plate/lobula columnar, type II (LPLC2) in Drosophila, and show how its selectivity is established by radial motion opponency. In the fly visual system, directionally selective small-field neurons called T4 and T5 form a spatial map in the lobula plate, where they each terminate in one of four retinotopic layers, such that each layer responds to motion in a different cardinal direction. Single-cell anatomical analysis reveals that each arm of the LPLC2 cross-shaped primary dendrites ramifies in one of these layers and extends along that layer{\textquoteright}s preferred motion direction. In vivo calcium imaging demonstrates that, as their shape predicts, individual LPLC2 neurons respond strongly to outward motion emanating from the centre of the neuron{\textquoteright}s receptive field. Each dendritic arm also receives local inhibitory inputs directionally selective for inward motion opposing the excitation. This radial motion opponency generates a balance of excitation and inhibition that makes LPLC2 non-responsive to related patterns of motion such as contraction, wide-field rotation or luminance change. As a population, LPLC2 neurons densely cover visual space and terminate onto the giant fibre descending neurons, which drive the jump muscle motor neuron to trigger an escape take off. Our findings provide a mechanistic description of the selective feature detection that flies use to discern and escape looming threats.

}, issn = {1476-4687}, doi = {10.1038/nature24626}, author = {Klapoetke, Nathan C and Nern, Aljoscha and Peek, Martin Y and Rogers, Edward M and Breads, Patrick and Rubin, Gerald M and Reiser, Michael B and Card, Gwyneth M} } @article {47932, title = {Comparative approaches to escape.}, journal = {Current Opinion in Neurobiology}, volume = {41}, year = {2016}, month = {2016 Oct 3}, pages = {167-173}, abstract = {

Neural circuits mediating visually evoked escape behaviors are promising systems in which to dissect the neural basis of behavior. Behavioral responses to predator-like looming stimuli, and their underlying neural computations, are remarkably similar across species. Recently, genetic tools have been applied in this classical paradigm, revealing novel non-cortical pathways that connect loom processing to defensive behaviors in mammals and demonstrating that loom encoding models from locusts also fit vertebrate neural responses. In both invertebrates and vertebrates, relative spike-timing in descending pathways is a mechanism for escape behavior choice. Current findings suggest that experimentally tractable systems, such as Drosophila, may be applicable models for sensorimotor processing and persistent states in higher organisms.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2016.09.012}, author = {Peek, Martin Y and Card, Gwyneth M} } @article {47524, title = {Genetic and environmental control of neurodevelopmental robustness in Drosophila.}, journal = {PLoS One}, volume = {11}, year = {2016}, month = {2016 May 25}, pages = {e0155957}, abstract = {

Interindividual differences in neuronal wiring may contribute to behavioral individuality and affect susceptibility to neurological disorders. To investigate the causes and potential consequences of wiring variation in Drosophila melanogaster, we focused on a hemilineage of ventral nerve cord interneurons that exhibits morphological variability. We find that late-born subclasses of the 12A hemilineage are highly sensitive to genetic and environmental variation. Neurons in the second thoracic segment are particularly variable with regard to two developmental decisions, whereas its segmental homologs are more robust. This variability "hotspot" depends on Ultrabithorax expression in the 12A neurons, indicating variability is cell-intrinsic and under genetic control. 12A development is more variable and sensitive to temperature in long-established laboratory strains than in strains recently derived from the wild. Strains with a high frequency of one of the 12A variants also showed a high frequency of animals with delayed spontaneous flight initiation, whereas other wing-related behaviors did not show such a correlation and were thus not overtly affected by 12A variation. These results show that neurodevelopmental robustness is variable and under genetic control in Drosophila and suggest that the fly may serve as a model for identifying conserved gene pathways that stabilize wiring in stressful developmental environments. Moreover, some neuronal lineages are variation hotspots and thus may be more amenable to evolutionary change.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0155957}, author = {Mellert, David J and Williamson, W Ryan and Shirangi, Troy R and Card, Gwyneth M and Truman, James W} } @article {48136, title = {Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs.}, journal = {eLife}, volume = {5}, year = {2016}, month = {2016 Dec 28}, abstract = {

Visual projection neurons (VPNs) provide an anatomical connection between early visual processing and higher brain regions. Here we characterize lobula columnar (LC) cells, a class of Drosophila VPNs that project to distinct central brain structures called optic glomeruli. We anatomically describe 22 different LC types and show that, for several types, optogenetic activation in freely moving flies evokes specific behaviors. The activation phenotypes of two LC types closely resemble natural avoidance behaviors triggered by a visual loom. In vivo two-photon calcium imaging reveals that these LC types respond to looming stimuli, while another type does not, but instead responds to the motion of a small object. Activation of LC neurons on only one side of the brain can result in attractive or aversive turning behaviors depending on the cell type. Our results indicate that LC neurons convey information on the presence and location of visual features relevant for specific behaviors.

}, issn = {2050-084X}, doi = {10.7554/eLife.21022}, author = {Wu, Ming and Nern, Aljoscha and Williamson, W Ryan and Morimoto, Mai M and Reiser, Michael B and Card, Gwyneth M and Rubin, Gerald M} } @article {45165, title = {A spike-timing mechanism for action selection.}, journal = {Nature Neuroscience}, volume = {17}, year = {2014}, month = {2014 Jul 17}, pages = {962-70}, abstract = {

We discovered a bimodal behavior in the genetically tractable organism Drosophila melanogaster that allowed us to directly probe the neural mechanisms of an action selection process. When confronted by a predator-mimicking looming stimulus, a fly responds with either a long-duration escape behavior sequence that initiates stable flight or a distinct, short-duration sequence that sacrifices flight stability for speed. Intracellular recording of the descending giant fiber (GF) interneuron during head-fixed escape revealed that GF spike timing relative to parallel circuits for escape actions determined which of the two behavioral responses was elicited. The process was well described by a simple model in which the GF circuit has a higher activation threshold than the parallel circuits, but can override ongoing behavior to force a short takeoff. Our findings suggest a neural mechanism for action selection in which relative activation timing of parallel circuits creates the appropriate motor output.

}, issn = {1546-1726}, doi = {10.1038/nn.3741}, author = {von Reyn, Catherine R and Breads, Patrick and Peek, Martin Y and Zheng, Grace Zhiyu and Williamson, W Ryan and Yee, Alyson L and Leonardo, Anthony and Card, Gwyneth M} } @article {5321, title = {Escape behaviors in insects.}, journal = {Current Opinion in Neurobiology}, volume = {22}, year = {2012}, month = {2012 Apr}, pages = {180-6}, abstract = {

Escape behaviors are, by necessity, fast and robust, making them excellent systems with which to study the neural basis of behavior. This is especially true in insects, which have comparatively tractable nervous systems and members who are amenable to manipulation with genetic tools. Recent technical developments in high-speed video reveal that, despite their short duration, insect escape behaviors are more complex than previously appreciated. For example, before initiating an escape jump, a fly performs sophisticated posture and stimulus-dependent preparatory leg movements that enable it to jump away from a looming threat. This newfound flexibility raises the question of how the nervous system generates a behavior that is both rapid and flexible. Recordings from the cricket nervous system suggest that synchrony between the activity of specific interneuron pairs may provide a rapid cue for the cricket to detect the direction of an approaching predator and thus which direction it should run. Technical advances make possible wireless recording from neurons while locusts escape from a looming threat, enabling, for the first time, a direct correlation between the activity of multiple neurons and the time-course of an insect escape behavior.

}, keywords = {Animals, Escape Reaction, Ethology, Insects, Neural Pathways, Neurology, Neurons}, issn = {1873-6882}, doi = {10.1016/j.conb.2011.12.009}, author = {Card, Gwyneth M} } @conference {48656, title = {Dynamics of escaping flight initiations of Drosophila melanogaster.}, booktitle = {2nd IEEE RAS \& EMBS International Conference on Biomedical Robotics and Biomechatronics}, year = {2009}, month = {2008}, abstract = {

We present a reconstruction of the dynamics of flight initiation from kinematic data extracted from high-speed video recordings of the fruit fly Drosophila melanogaster. The dichotomy observed in this insect{\textquoteright}s flight initiation sequences, generated by the presence or absence of visual stimuli, clearly generates two contrasting sets of dynamics once the flies become airborne. By calculating reaction forces and moments using the unconstrained motion formulation for a rigid body, we assess the fly{\textquoteright}s responses amidst these two dynamic patterns as a step towards refining our understanding of insect flight control.

}, doi = {10.1109/BIOROB.2008.4762921}, author = {Zabalax, Francisco A and Card, Gwyneth M and Fontaine, Ebraheem I and Murray, Richard M and Dickinson, Michael H} } @article {2168, title = {Flight dynamics and control of evasive maneuvers: the fruit fly{\textquoteright}s takeoff.}, journal = {IEEE Transactions on Bio-Medical Engineering}, volume = {56}, year = {2009}, month = {2009 Sep}, pages = {2295-8}, abstract = {

We have approached the problem of reverse-engineering the flight control mechanism of the fruit fly by studying the dynamics of the responses to a visual stimulus during takeoff. Building upon a prior framework [G. Card and M. Dickinson, J. Exp. Biol., vol. 211, pp. 341-353, 2008], we seek to understand the strategies employed by the animal to stabilize attitude and orientation during these evasive, highly dynamical maneuvers. As a first step, we consider the dynamics from a gray-box perspective: examining lumped forces produced by the insect{\textquoteright}s legs and wings. The reconstruction of the flight initiation dynamics, based on the unconstrained motion formulation for a rigid body, allows us to assess the fly{\textquoteright}s responses to a variety of initial conditions induced by its jump. Such assessment permits refinement by using a visual tracking algorithm to extract the kinematic envelope of the wings [E. I. Fontaine, F. Zabala, M. Dickinson, and J. Burdick, "Wing and body motion during flight initiation in Drosophila revealed by automated visual tracking," submitted for publication] in order to estimate lift and drag forces [F. Zabala, M. Dickinson, and R. Murray, "Control and stability of insect flight during highly dynamical maneuvers," submitted for publication], and recording actual leg-joint kinematics and using them to estimate jump forces [F. Zabala, "A bio-inspired model for directionality control of flight initiation," to be published.]. In this paper, we present the details of our approach in a comprehensive manner, including the salient results.

}, keywords = {Algorithms, Animal, Animals, Biomechanics, Computer-Assisted, Drosophila melanogaster, Escape Reaction, Flight, Image Processing, Video Recording}, issn = {1558-2531}, doi = {10.1109/TBME.2009.2027606}, author = {Zabala, Francisco A and Card, Gwyneth M and Fontaine, Ebraheem I and Dickinson, Michael H and Murray, Richard M} } @article {48655, title = {High-speed pollen release in the white mulberry tree, Morus alba L.}, journal = {Sexual Plant Reproduction}, volume = {19}, year = {2006}, month = {2006 Mar}, pages = {19-24}, abstract = {

Anemophilous plants described as catapulting pollen explosively into the air have rarely attracted detailed examination. We investigated floral anthesis in a male mulberry tree with high-speed video and a force probe. The stamen was inflexed within the floral bud. Exposure to dry air initially resulted in a gradual movement of the stamen. This caused fine threads to tear at the stomium, ensuring dehiscence of the anther, and subsequently enabled the anther to slip off a restraining pistillode. The sudden release of stored elastic energy in the spring-like filament drove the stamen to straighten in less than 25\ μs, and reflex the petals to velocities in excess of half the speed of sound. This is the fastest motion yet observed in biology, and approaches the theoretical physical limits for movements in plants.

}, doi = {10.1007/s00497-005-0018-9}, author = {Taylor, Philip E and Card, Gwyneth M and House, James and Dickinson, Michael H and Flagan, Richard C} }