@article {67657, title = {Disrupting cortico-cerebellar communication impairs dexterity.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Jul 29}, abstract = {

To control reaching, the nervous system must generate large changes in muscle activation to drive the limb toward the target, and must also make smaller adjustments for precise and accurate behavior. Motor cortex controls the arm through projections to diverse targets across the central nervous system, but it has been challenging to identify the roles of cortical projections to specific targets. Here, we selectively disrupt cortico-cerebellar communication in the mouse by optogenetically stimulating the pontine nuclei in a cued reaching task. This perturbation did not typically block movement initiation, but degraded the precision, accuracy, duration, or success rate of the movement. Correspondingly, cerebellar and cortical activity during movement were largely preserved, but differences in hand velocity between control and stimulation conditions predicted from neural activity were correlated with observed velocity differences. These results suggest that while the total output of motor cortex drives reaching, the cortico-cerebellar loop makes small adjustments that contribute to the successful execution of this dexterous movement.

}, issn = {2050-084X}, doi = {10.7554/eLife.65906}, author = {Guo, Jian-Zhong and Sauerbrei, Britton A and Cohen, Jeremy D and Mischiati, Matteo and Graves, Austin R and Pisanello, Ferruccio and Branson, Kristin M and Hantman, Adam W} } @article {65816, title = {Cell types and neuronal circuitry underlying female aggression in Drosophila.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Nov 03}, abstract = {

Aggressive social interactions are used to compete for limited resources and are regulated by complex sensory cues and the organism{\textquoteright}s internal state. While both sexes exhibit aggression, its neuronal underpinnings are understudied in females. Here, we identify a population of sexually dimorphic aIPg neurons in the adult central brain whose optogenetic activation increased, and genetic inactivation reduced, female aggression. Analysis of GAL4 lines identified in an unbiased screen for increased female chasing behavior revealed the involvement of another sexually dimorphic neuron, pC1d, and implicated aIPg and pC1d neurons as core nodes regulating female aggression. Connectomic analysis demonstrated that aIPg neurons and pC1d are interconnected and suggest that aIPg neurons may exert part of their effect by gating the flow of visual information to descending neurons. Our work reveals important regulatory components of the neuronal circuitry that underlies female aggressive social interactions and provides tools for their manipulation.

}, issn = {2050-084X}, doi = {10.7554/eLife.58942}, author = {Schretter, Catherine E and Aso, Yoshi and Robie, Alice A and Dreher, Marisa and Dolan, Michael-John and Chen, Nan and Ito, Masayoshi and Yang, Tansy and Parekh, Ruchi and Branson, Kristin M and Rubin, Gerald M} } @conference {49317, title = {Honeybee detection and pose estimation using convolutional neural networks.}, booktitle = {RFIAP 2018}, year = {2018}, month = {2018 Jun 26}, abstract = {

The ability to automatize the analysis of video for monitoring animals and insects is of great interest for behavior science and ecology [1]. In particular, honeybees play a crucial role in agriculture as natural pollinators. However, recent studies has shown that phenomena such as colony collapse disorder are causing the loss of many colonies [2]. Due to the high number of interacting factors to explain these events, a multi-faceted analysis of the bees in their environment is required. We focus in our work in developing tools to help model and understand their behavior as individuals, in relation with the health and performance of the colony.

In this paper, we report the development of a new system for the detection, locali- zation and tracking of honeybee body parts from video on the entrance ramp of the colony. The proposed system builds on the recent advances in Convolutional Neu- ral Networks (CNN) for Human pose estimation and evaluates the suitability for the detection of honeybee pose as shown in Figure 1. This opens the door for novel animal behavior analysis systems that take advantage of the precise detection and tracking of the insect pose.\ 

}, url = {https://rfiap2018.ign.fr/sites/default/files/ARTICLES/RFIAP_2018/RFIAP_2018_Felipe_Honeybee.pdf}, author = {Rodriguez, Ivan F and Branson, Kristin M and Acuna, Edgar and Agosto-Rivera, Jose and Giray, Tugrul and Megret, Remi} } @article {49585, title = {Motor cortex is an input-driven dynamical system controlling dexterous movement.}, journal = {bioRxiv}, year = {2018}, month = {2018-12-14}, pages = {266320}, abstract = {

Skillful control of movement is central to our ability to sense and manipulate the world. A large body of work in nonhuman primates has demonstrated that motor cortex provides flexible, time-varying activity patterns that control the arm during reaching and grasping. Previous studies have suggested that these patterns are generated by strong local recurrent dynamics operating autonomously from inputs during movement execution. An alternative possibility is that motor cortex requires coordination with upstream brain regions throughout the entire movement in order to yield these patterns. Here, we developed an experimental preparation in the mouse to directly test these possibilities using optogenetics and electrophysiology during a skilled reach-to-grab-to-eat task. To validate this preparation, we first established that a specific, time-varying pattern of motor cortical activity was required to produce coordinated movement. Next, in order to disentangle the contribution of local recurrent motor cortical dynamics from external input, we optogenetically held the recurrent contribution constant, then observed how motor cortical activity recovered following the end of this perturbation. Both the neural responses and hand trajectory varied from trial to trial, and this variability reflected variability in external inputs. To directly probe the role of these inputs, we used optogenetics to perturb activity in the thalamus. Thalamic perturbation at the start of the trial prevented movement initiation, and perturbation at any stage of the movement prevented progression of the hand to the target; this demonstrates that input is required throughout the movement. By comparing motor cortical activity with and without thalamic perturbation, we were able to estimate the effects of external inputs on motor cortical population activity. Thus, unlike pattern-generating circuits that are local and autonomous, such as those in the spinal cord that generate left-right alternation during locomotion, the pattern generator for reaching and grasping is distributed across multiple, strongly-interacting brain regions.

}, doi = {10.1101/266320}, author = {Sauerbrei, Britton and Guo, Jian-Zhong and Mischiati, Matteo and Guo, Wendy and Kabra, Mayank and Verma, Nakul and Branson, Kristin M and Hantman, Adam W} } @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 {48919, title = {Network-size independent covering number bounds for deep networks.}, journal = {arXiv}, year = {2017}, month = {2017 Nov 02}, pages = {arXiv:1711.00753 }, abstract = {

We give a covering number bound for deep learning networks that is independent of the size of the network. The key for the simple analysis is that for linear classifiers, rotating the data doesn{\textquoteright}t affect the covering number. Thus, we can ignore the rotation part of each layer{\textquoteright}s linear transformation, and get the covering number bound by concentrating on the scaling part.

}, url = {https://arxiv.org/abs/1711.00753}, author = {Kabra, Mayank and Branson, Kristin M} } @proceedings {47288, title = {Sample complexity of learning Mahalanobis distance metrics.}, volume = {28}, year = {2015}, month = {2015-Jul }, address = {Montreal, Canada}, abstract = {

Metric learning seeks a transformation of the feature space that enhances prediction quality for a given task. In this work we provide PAC-style sample complexity rates for supervised metric learning. We give matching lower- and upper-bounds showing that sample complexity scales with the representation dimension when no assumptions are made about the underlying data distribution. In addition, by leveraging the structure of the data distribution, we provide rates fine-tuned to a specific notion of the intrinsic complexity of a given dataset, allowing us to relax the dependence on representation dimension. We show both theoretically and empirically that augmenting the metric learning optimization criterion with a simple norm-based regularization is important and can help adapt to a dataset{\textquoteright}s intrinsic complexity yielding better generalization, thus partly explaining the empirical success of similar\ regularizations\ reported in previous works.

}, keywords = {metric learning, sample complexity}, url = {https://papers.nips.cc/paper/5811-sample-complexity-of-learning-mahalanobis-distance-metrics}, author = {Verma, Nakul and Branson, Kristin M} } @article {46984, title = {Whole-central nervous system functional imaging in larval Drosophila.}, journal = {Nature Communications}, volume = {6}, year = {2015}, month = {2015 Aug 11}, pages = {7924}, abstract = {

Understanding how the brain works in tight concert with the rest of the central nervous system (CNS) hinges upon knowledge of coordinated activity patterns across the whole CNS. We present a method for measuring activity in an entire, non-transparent CNS with high spatiotemporal resolution. We combine a light-sheet microscope capable of simultaneous multi-view imaging at volumetric speeds 25-fold faster than the state-of-the-art, a whole-CNS imaging assay for the isolated Drosophila larval CNS and a computational framework for analysing multi-view, whole-CNS calcium imaging data. We image both brain and ventral nerve cord, covering the entire CNS at 2 or 5 Hz with two- or one-photon excitation, respectively. By mapping network activity during fictive behaviours and quantitatively comparing high-resolution whole-CNS activity maps across individuals, we predict functional connections between CNS regions and reveal neurons in the brain that identify type and temporal state of motor programs executed in the ventral nerve cord.

}, doi = {10.1038/ncomms8924}, author = {Lemon, William C and Pulver, Stefan R and H{\"o}ckendorf, Burkhard and McDole, Katie and Branson, Kristin M and Freeman, Jeremy and Keller, Philipp J} } @article {45811, title = {Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila.}, journal = {eLife}, volume = {4}, year = {2014}, month = {12/2014}, abstract = {

Animals discriminate stimuli, learn their predictive value and use this knowledge to modify their behavior. In Drosophila, the mushroom body (MB) plays a key role in these processes. Sensory stimuli are sparsely represented by \~{}2000 Kenyon cells, which converge onto 34 output neurons (MBONs) of 21 types. We studied the role of MBONs in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network. We also show that optogenetic activation of MBONs can, depending on cell type, induce repulsion or attraction in flies. The behavioral effects of MBON perturbation are combinatorial, suggesting that the MBON ensemble collectively represents valence. We propose that local, stimulus-specific dopaminergic modulation selectively alters the balance within the MBON network for those stimuli. Our results suggest that valence encoded by the MBON ensemble biases memory-based action selection.

}, issn = {2050-084X}, doi = {10.7554/eLife.04580}, author = {Aso, Yoshinori and Sitaraman, Divya and Ichinose, Toshiharu and Kaun, Karla R and Vogt, Katrin and Belliart-Gu{\'e}rin, Ghislain and Placais, Pierre-Yves and Robie, Alice A and Yamagata, Nobuhiro and Schnaitmann, Christopher and Rowell, William J and Johnston, Rebecca M and Ngo, Teri-T B and Chen, Nan and Korff, Wyatt and Nitabach, Michael N and Heberlein, Ulrike and Preat, Thomas and Branson, Kristin M and Tanimoto, Hiromu and Rubin, Gerald M} }