@article {69679,
	title = {Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 Nov 22},
	abstract = {<p>The body of an animal influences how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly Drosophila melanogaster in the MuJoCo physics engine. Our model is general-purpose, enabling the simulation of diverse fly behaviors, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion, both flight and walking. To support these behaviors, we have extended MuJoCo with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. We demonstrate the use of visual sensors and the re-use of a pre-trained general-purpose flight controller by training the model to perform visually guided flight tasks. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context.Competing Interest StatementThe authors have declared no competing interest.</p>
},
	doi = {10.1101/2024.03.11.584515},
	url = {https://www.biorxiv.org/content/early/2024/11/22/2024.03.11.584515},
	author = {Vaxenburg, Roman and Siwanowicz, Igor and Merel, Josh and Robie, Alice A. and Morrow, Carmen and Novati, Guido and Stefanidi, Zinovia and Both, Gert-Jan and Card, Gwyneth M. and Reiser, Michael B. and Botvinick, Matthew M. and Branson, Kristin M. and Tassa, Yuval and Turaga, Srinivas C.}
}
@article {47262,
	title = {Simultaneous denoising, deconvolution, and demixing of calcium imaging data.},
	journal = {Neuron},
	volume = {89},
	year = {2016},
	month = {2016 Jan 20},
	pages = {285-99},
	abstract = {<p>We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in\&nbsp;vitro and in\&nbsp;vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.</p>
},
	issn = {1097-4199},
	doi = {10.1016/j.neuron.2015.11.037},
	author = {Pnevmatikakis, Eftychios A and Soudry, Daniel and Gao, Yuanjun and Machado, Timothy A and Merel, Josh and Pfau, David and Reardon, Thomas and Mu, Yu and Lacefield, Clay and Yang, Weijian and Ahrens, Misha and Bruno, Randy and Jessell, Thomas M and Peterka, Darcy S and Yuste, Rafael and Paninski, Liam}
}
