@article {51252, title = {Comparisons between the ON- and OFF-edge motion pathways in the brain.}, journal = {eLife}, volume = {8}, year = {2019}, month = {2019 Jan 09}, abstract = {

Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In , recent synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest an increasingly intricate motion model compared with the ubiquitous Hassenstein-Reichardt model, while our knowledge of OFF-pathway (T5) has been incomplete. Here we present a conclusive and comprehensive connectome that for the first time integrates detailed connectivity information for inputs to both T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. While the two pathways are likely evolutionarily linked and indeed exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

}, issn = {2050-084X}, doi = {10.7554/eLife.40025}, author = {Shinomiya, Kazunori and Huang, Gary and Lu, Zhiyuan and Parag, Toufiq and Xu, C Shan and Aniceto, Roxanne and Ansari, Namra and Cheatham, Natasha and Lauchie, Shirley and Neace, Erika and Ogundeyi, Omotara and Ordish, Christopher and Peel, David and Shinomiya, Aya and Smith, Claire and Takemura, Satoko and Talebi, Iris and Rivlin, Patricia K and Nern, Aljoscha and Scheffer, Louis K and Plaza, Stephen M and Meinertzhagen, Ian A} } @article {48658, title = {A connectome of a learning and memory center in the adult Drosophila brain.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 Jul 18}, pages = {e26975}, abstract = {

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In\ Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB{\textquoteright}s α lobe, using a dataset of isotropic 8-nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple\ en passant\ synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only six percent of KC\>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC\>DAN and DAN\>MBON. DAN activation produces a slow depolarization of the MBON in these DAN\>MBON synapses and can weaken memory recall.

}, doi = {10.7554/eLife.26975}, author = {Takemura, Shin-ya and Aso, Yoshinori and Hige, Toshihide and Wong, Allan M and Lu, Zhiyuan and Xu, C Shan and Rivlin, Patricia K and Hess, Harald F and Zhao, Ting and Parag, Toufiq and Berg, Stuart and Huang, Gary and Katz, William T and Olbris, Donald J and Plaza, Stephen M and Umayam, Lowell A and Aniceto, Roxanne and Chang, Lei-Ann and Lauchie, Shirley and et al} } @proceedings {47837, title = {Efficient classifier training to minimize false merges in electron microscopy segmentation.}, year = {2015}, month = {2015}, pages = {657-65}, author = {Parag, Toufiq and Ciresan, Dan and Giusti, Alessandro} } @article {47152, title = {Synaptic circuits and their variations within different columns in the visual system of Drosophila.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {112}, year = {2015}, month = {2015 Nov 3}, pages = {13711-6}, abstract = {

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly{\textquoteright}s compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla{\textquoteright}s neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, \<1\% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form >=20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.

}, issn = {1091-6490}, doi = {10.1073/pnas.1509820112}, author = {Takemura, Shin-ya and Xu, C Shan and Lu, Zhiyuan and Rivlin, Patricia K and Parag, Toufiq and Olbris, Donald J and Plaza, Stephen and Zhao, Ting and Katz, William T and Umayam, Lowell and Weaver, Charlotte and Hess, Harald F and Horne, Jane Anne and Nunez-Iglesias, Juan and Aniceto, Roxanne and Chang, Lei-Ann and Lauchie, Shirley and Nasca, Ashley and Ogundeyi, Omotara and Sigmund, Christopher and Takemura, Satoko and Tran, Julie and Langille, Carlie and Le Lacheur, Kelsey and McLin, Sari and Shinomiya, Aya and Chklovskii, Dmitri B and Meinertzhagen, Ian A and Scheffer, Louis K} } @article {46226, title = {Annotating synapses in large EM datasets.}, journal = {arXiv}, year = {2014}, month = {2014 Sep 5}, pages = {arXiv:1409.1801 [q-bio.QM]}, abstract = {

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders.

}, url = {http://arxiv.org/abs/1409.1801}, author = {Plaza, Stephen M and Parag, Toufiq and Huang, Gary and Olbris, Donald J. and Saunders, Mathew A. and Rivlin, Patricia K.} } @article {46224, title = {A context-aware delayed agglomeration framework for EM segmentation.}, journal = {arXiv}, year = {2014}, month = {2014 Jun 5}, pages = {arXiv:1406.1476 [cs.CV]}, abstract = {

This paper proposes a novel agglomerative framework for Electron Microscopy (EM) image (or volume) segmentation. For the overall segmentation methodology, we propose a context-aware algorithm that clusters the over-segmented regions of different sub-classes (representing different biological entities) in different stages. Furthermore, a delayed scheme for agglomerative clustering, which postpones the merge of newly formed bodies, is also proposed to generate a more confident boundary prediction. We report significant improvements in both segmentation accuracy and speed attained by the proposed approaches over existing standard methods on both 2D and 3D datasets.

}, url = {http://arxiv.org/abs/1406.1476}, author = {Parag, Toufiq and Chakraborty, Anirban and Plaza, Stephen M} } @article {46228, title = {Small sample learning of superpixel classifiers for EM segmentation- extended version.}, journal = {arXiv}, year = {2014}, month = {2014 Jun 6}, pages = {arXiv:1406.1774 [cs.CV]}, abstract = {

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (\<20\%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

}, url = {http://arxiv.org/abs/1406.1774}, author = {Parag, Toufiq and Plaza, Stephen M and Scheffer, Louis K} } @proceedings {46298, title = {Small sample learning of superpixel classifiers for EM segmentation.}, volume = {17}, year = {2014}, month = {2014}, pages = {389-97}, abstract = {

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is {\textquoteright}active semi-supervised{\textquoteright} because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (\< 20\%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

}, keywords = {Algorithms, Artificial Intelligence, Cells, Cultured, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Microscopy, Electron, Transmission, Neurites, Pattern Recognition, Automated, Reproducibility of Results, Sample Size, Sensitivity and Specificity}, author = {Parag, Toufiq and Plaza, Stephen and Scheffer, Louis} } @conference {2844, title = {Supervised hypergraph labeling.}, booktitle = {IEEE Conference on Computer Vision Pattern Recognition}, year = {2011}, month = {2011}, abstract = {

We address the problem of labeling individual datapoints given some knowledge about (small) subsets or groups of them. The knowledge we have for a group is the likelihood value for each group member to satisfy a certain model. This problem is equivalent to hypergraph labeling problem where each datapoint corresponds to a node and the each subset correspond to a hyperedge with likelihood value as its weight. We propose a novel method to model the label dependence using an Undirected Graphical Model and reduce the problem of hypergraph labeling into an inference problem. This paper describes the structure and necessary components of such model and proposes useful cost functions. We discuss the behavior of proposed algorithm with different forms of the cost functions, identify suitable algorithms for inference and analyze required properties when it is theoretically guaranteed to have exact solution. Examples of several real world problems are shown as applications of the proposed method.

}, author = {Parag, Toufiq and Elgammal, Ahmed} } @conference {2845, title = {Boosting adaptive linear weak classifiers for online learning and tracking.}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2008}, month = {2008 Jun 28}, author = {Parag, Toufiq and Porikli, Fatih and Elgammal, Ahmed} } @conference {2846, title = {A framework for feature selection for background subtraction.}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2006}, month = {2006}, author = {Parag, Toufiq and Elgammal, Ahmed and Mittal, Anurag} } @conference {2847, title = {Unsupervised learning of boosted tree classifier using graph cuts for hand pose recognition.}, booktitle = {British Machine Vision Conference}, year = {2006}, month = {2006 Sep 4}, author = {Parag, Toufiq and Elgammal, Ahmed} }