@article {69957, title = {Compressive streak microscopy for fast sampling of fluorescent reporters of neural activity.}, journal = {Neurophotonics}, volume = {12}, year = {2025}, month = {2025 Apr 12}, pages = {025013}, abstract = {

SIGNIFICANCE: one-photon fluorescence imaging of calcium and voltage indicators expressed in neurons enables noninvasive recordings of neural activity with submillisecond precision. However, data acquisition speed is limited by the frame rate of cameras.

AIM: We developed a compressive streak fluorescence microscope to record fluorescence in individual neurons at high speeds ( frames per second) exceeding the nominal frame rate of the camera by trading off spatial pixels for temporal resolution.

APPROACH: Our microscope leverages a digital micromirror device for targeted illumination, a galvo mirror for temporal scanning, and a ridge regression algorithm for fast computational reconstruction of fluorescence traces with high temporal resolution.

RESULTS: In simulations, the ridge regression algorithm reconstructs traces of high temporal resolution with limited signal loss. Validation experiments with fluorescent beads and experiments in larval zebrafish demonstrate accurate reconstruction with a data compression ratio of 10 and accurate recordings of neural activity with 200- to 400-Hz sampling speeds.

CONCLUSIONS: Our compressive microscopy enables new experimental capabilities to monitor activity at a sampling speed that outpaces the nominal frame rate of the camera.

}, issn = {2329-423X}, doi = {10.1117/1.NPh.12.2.025013}, url = {https://doi.org/10.1117/1.nph.12.2.025013}, author = {Cai, Changjia and Traubert, Owen and Tormes-Vaquerano, Jovan and Eybposh, M Hossein and Turaga, Srinivas C and Rodriguez-Romaguera, Jose and Naumann, Eva A and P{\'e}gard, Nicolas C} } @article {68564, title = {Local shape descriptors for neuron segmentation.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Feb 01}, pages = {295-303}, abstract = {

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

}, issn = {1548-7105}, doi = {10.1038/s41592-022-01711-z}, author = {Sheridan, Arlo and Nguyen, Tri M and Deb, Diptodip and Lee, Wei-Chung Allen and Saalfeld, Stephan and Turaga, Srinivas C and Manor, Uri and Funke, Jan} } @article {65798, title = {Behavioral state coding by molecularly defined paraventricular hypothalamic cell type ensembles.}, journal = {Science}, volume = {370}, year = {2020}, month = {2020 Oct 16}, abstract = {

Brains encode behaviors using neurons amenable to systematic classification by gene expression. The contribution of molecular identity to neural coding is not understood because of the challenges involved with measuring neural dynamics and molecular information from the same cells. We developed CaRMA (calcium and RNA multiplexed activity) imaging based on recording in vivo single-neuron calcium dynamics followed by gene expression analysis. We simultaneously monitored activity in hundreds of neurons in mouse paraventricular hypothalamus (PVH). Combinations of cell-type marker genes had predictive power for neuronal responses across 11 behavioral states. The PVH uses combinatorial assemblies of molecularly defined neuron populations for grouped-ensemble coding of survival behaviors. The neuropeptide receptor neuropeptide Y receptor type 1 (Npy1r) amalgamated multiple cell types with similar responses. Our results show that molecularly defined neurons are important processing units for brain function.

}, issn = {1095-9203}, doi = {10.1126/science.abb2494}, author = {Xu, Shengjin and Yang, Hui and Menon, Vilas and Lemire, Andrew L and Wang, Lihua and Henry, Fredrick E and Turaga, Srinivas C and Sternson, Scott M} } @article {65128, title = {Constraining computational models using electron microscopy wiring diagrams.}, journal = {Current Opinion in Neurobiology}, volume = {58}, year = {2019}, month = {2019 Aug 27}, pages = {94-100}, abstract = {

Numerous efforts to generate "connectomes," or synaptic wiring diagrams, of large neural circuits or entire nervous systems are currently underway. These efforts promise an abundance of data to guide theoretical models of neural computation and test their predictions. However, there is not yet a standard set of tools for incorporating the connectivity constraints that these datasets provide into the models typically studied in theoretical neuroscience. This article surveys recent approaches to building models with constrained wiring diagrams and the insights they have provided. It also describes challenges and the need for new techniques to scale these approaches to ever more complex datasets.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2019.07.007}, author = {Litwin-Kumar, Ashok and Turaga, Srinivas C} } @article {48806, title = {Large scale image segmentation with structured loss based deep learning for connectome reconstruction.}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {41}, year = {2019}, month = {2019 Jul 1}, pages = {1669-80}, abstract = {

We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a 3D U-net, trained to predict affinities between voxels, followed by iterative region agglomeration. We train using a structured loss based on MALIS, encouraging topologically correct segmentations obtained from affinity thresholding. Our extension consists of two parts: First, we present a quasi-linear method to compute the loss gradient, improving over the original quadratic algorithm. Second, we compute the gradient in two separate passes to avoid spurious gradient contributions in early training stages. Our predictions are accurate enough that simple learning-free percentile-based agglomeration outperforms more involved methods used earlier on inferior predictions. We present results on three diverse EM datasets, achieving relative improvements over previous results of 27\%, 15\%, and 250\%. Our findings suggest that a single method can be applied to both nearly isotropic block-face EM data and anisotropic serial sectioned EM data. The runtime of our method scales linearly with the size of the volume and achieves a throughput of ~2.6 seconds per megavoxel, qualifying our method for the processing of very large datasets.

}, doi = {10.1109/TPAMI.2018.2835450}, author = {Funke, Jan and Tschopp, Fabian D and Grisaitis, William and Sheridan, Arlo and Singh, Chandan and Saalfeld, Stephan and Turaga, Srinivas C} } @article {49290, title = {A connectome based hexagonal lattice convolutional network model of the Drosophila visual system.}, journal = {arXiv}, year = {2018}, month = {2018 Jun 12}, pages = {1806.04793 }, abstract = {

What can we learn from a connectome? We constructed a simplified model of the first two stages of the fly visual system, the lamina and medulla. The resulting hexagonal lattice convolutional network was trained using backpropagation through time to perform object tracking in natural scene videos. Networks initialized with weights from connectome reconstructions automatically discovered well-known orientation and direction selectivity properties in T4 neurons and their inputs, while networks initialized at random did not. Our work is the first demonstration, that knowledge of the connectome can enable in silico predictions of the functional properties of individual neurons in a circuit, leading to an understanding of circuit function from structure alone.

}, url = {https://arxiv.org/abs/1806.04793}, author = {Tschopp, Fabian D and Reiser, Michael B and Turaga, Srinivas C} } @article {49255, title = {Discrete flow posteriors for variational inference in discrete dynamical systems.}, journal = {arXiv}, year = {2018}, month = {2018 May 28}, pages = {1805.10958}, abstract = {

Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism. However, such simple approximate posteriors are often insufficient, as they eliminate statistical dependencies in the posterior. While it is possible to use normalizing flow approximate posteriors for continuous latents, some problems have discrete latents and strong statistical dependencies. The most natural approach to model these dependencies is an autoregressive distribution, but sampling from such distributions is inherently sequential and thus slow. We develop a fast, parallel sampling procedure for autoregressive distributions based on fixed-point iterations which enables efficient and accurate variational inference in discrete state-space latent variable dynamical systems. To optimize the variational bound, we considered two ways to evaluate probabilities: inserting the relaxed samples directly into the pmf for the discrete distribution, or converting to continuous logistic latent variables and interpreting the K-step fixed-point iterations as a normalizing flow. We found that converting to continuous latent variables gave considerable additional scope for mismatch between the true and approximate posteriors, which resulted in biased inferences, we thus used the former approach. Using our fast sampling procedure, we were able to realize the benefits of correlated posteriors, including accurate uncertainty estimates for one cell, and accurate connectivity estimates for multiple cells, in an order of magnitude less time.

}, url = {https://arxiv.org/abs/1805.10958}, author = {Aitchison, Laurence and Adam, Vincent and Turaga, Srinivas C} } @article {49466, title = {In toto imaging and reconstruction of post-implantation mouse development at the single-cell level.}, journal = {Cell}, volume = {175}, year = {2018}, month = {2018 Oct 10}, pages = {859-876}, abstract = {

The mouse embryo has long been central to the study of mammalian development; however, elucidating the cell behaviors governing gastrulation and the formation of tissues and organs remains a fundamental challenge. A major obstacle is the lack of live imaging and image analysis technologies capable of systematically following cellular dynamics across the developing embryo. We developed a light-sheet microscope that adapts itself to the dramatic changes in size, shape, and optical properties of the post-implantation\ mouse embryo and captures its development from gastrulation to early organogenesis at\ the cellular level. We furthermore developed a computational framework for reconstructing long-term cell tracks, cell divisions, dynamic fate maps, and maps of tissue morphogenesis across the entire embryo. By jointly analyzing cellular dynamics in multiple embryos registered in space and time, we built a dynamic atlas of post-implantation mouse development that, together with our microscopy and computational methods, is provided as a resource.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2018.09.031}, author = {McDole, Katie and Guignard, Leo and Amat, Fernando and Berger, Andrew and Malandain, Gr{\'e}goire and Royer, Lo{\"\i}c A and Turaga, Srinivas C and Branson, Kristin and Keller, Philipp J} } @article {49292, title = {Synaptic partner prediction from point annotations in insect brains.}, journal = {MICCAI 2018: Medical Image Computing and Computer Assisted Intervention}, year = {2018}, month = {2018 Sep 26}, abstract = {

High-throughput electron microscopy allows recording of lar- ge stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. Current efforts to automate this process focus mainly on the segmentation of neurons. However, in order to recover a wiring diagram, synaptic partners need to be identi- fied as well. This is especially challenging in insect brains like Drosophila melanogaster, where one presynaptic site is associated with multiple post- synaptic elements. Here we propose a 3D U-Net architecture to directly identify pairs of voxels that are pre- and postsynaptic to each other. To that end, we formulate the problem of synaptic partner identification as a classification problem on long-range edges between voxels to encode both the presence of a synaptic pair and its direction. This formulation allows us to directly learn from synaptic point annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and improve over the current state of the art, producing 3\% fewer errors than the next best method.

}, doi = {10.1007/978-3-030-00934-2_35}, author = {Buhmann, Julia and Krause, Renate and Lentini, Rodrigo Ceballos and Eckstein, Nils and Cook, Matthew and Turaga, Srinivas C and Funke, Jan} } @article {48836, title = {Community-based benchmarking improves spike inference from two-photon calcium imaging data.}, journal = {PLoS Computational Biology}, volume = {14}, year = {2017}, month = {2018 May 21}, pages = {e1006157}, abstract = {

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

}, doi = {10.1371/journal.pcbi.1006157}, author = {Berens, Philipp and Freeman, Jeremy and Deneux, Thomas and Chenkov, Nicolay and McColgan, Thomas and Speiser, Artur and Macke, Jakob H and Turaga, Srinivas C and Mineault, Patrick and Rupprecht, Peter and Gerhard, Stephan and Friedrich, Rainer W and Friedrich, Johannes and Paninski, Liam and Pachitariu, Marius and Harris, Kenneth D and Bolte, Ben and Machado, Timothy A and Ringach, Dario and etal} } @proceedings {48802, title = {Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations.}, year = {2017}, month = {2017 Dec 04}, abstract = {

A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements. We propose an approach for extracting low-dimensional dynamics from multiple, sequential recordings. Our algorithm scales to data comprising millions of observed dimensions, making it possible to access dynamics distributed across large populations or multiple brain areas. Building on subspace-identification approaches for dynamical systems, we perform parameter estimation by minimizing a moment-matching objective using a scalable stochastic gradient descent algorithm: The model is optimized to predict temporal covariations across neurons and across time. We show how this approach naturally handles missing data and multiple partial recordings, and can identify dynamics and predict correlations even in the presence of severe subsampling and small overlap between recordings. We demonstrate the effectiveness of the approach both on simulated data and a whole-brain larval zebrafish imaging dataset.\ 

}, url = {http://www.mackelab.org/pubs/Nonnenmacher_Turaga_NIPS_2017.pdf}, author = {Nonnenmacher, Marcel and Turaga, Srinivas C and Macke, Jakob H} } @proceedings {48805, title = {Fast amortized inference of neural activity from calcium imaging data with variational autoencoders.}, year = {2017}, month = {2017 Dec 04}, abstract = {

Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are necessary to infer the true underlying spiking activity from fluorescence measurements. Bayesian model inversion can be used to solve this problem, but typically requires either computationally expensive MCMC sampling, or faster but approximate maximum-a-posteriori optimization. Here, we introduce a flexible algorithmic framework for fast, efficient and accurate extraction of neural spikes from imaging data. Using the framework of variational autoencoders, we propose to amortize inference by training a deep neural network to perform model inversion efficiently. The recognition network is trained to produce samples from the posterior distribution over spike trains. Once trained, performing inference amounts to a fast single forward pass through the network, without the need for iterative optimization or sampling. We show that amortization can be applied flexibly to a wide range of nonlinear generative models and significantly improves upon the state of the art in computation time, while achieving competitive accuracy. Our framework is also able to represent posterior distributions over spike-trains. We demonstrate the generality of our method by proposing the first probabilistic approach for separating backpropagating action potentials from putative synaptic inputs in calcium imaging of dendritic spines.\ 

}, url = {http://www.mackelab.org/pubs/Speiser_NIPS_2017.pdf}, author = {Speiser, Artur and Yan, Jinyao and Archer, Evan and Buesing, Lars and Turaga, Srinivas C and Macke, Jakob H} } @proceedings {48942, title = {Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit.}, year = {2017}, month = {2017 Dec 04}, abstract = {

Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Our joint model includes at least two sets of discrete random variables; to avoid the dramatic slowdown typically caused by being unable to differentiate such variables, we introduce two strategies that have not, to our knowledge, been used with variational autoencoders. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.

}, url = {https://papers.nips.cc/paper/6940-model-based-bayesian-inference-of-neural-activity-and-connectivity-from-all-optical-interrogation-of-a-neural-circuit}, author = {Aitchison, Laurence and Russell, Lloyd and Packer, Adam M and Yan, Jinyao and Castonguaye, Philippe and H{\"a}usser, Michael and Turaga, Srinivas C} } @conference {67221, title = {Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit}, booktitle = {Advances in Neural Information Processing Systems}, year = {2017}, publisher = {Curran Associates, Inc.}, organization = {Curran Associates, Inc.}, abstract = {

Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.

}, url = {https://discovery.ucl.ac.uk/id/eprint/10072143/}, author = {Aitchison, Laurence and Russell, Lloyd and Packer, Adam M and Yan, Jinyao and Castonguay, Philippe and H{\"a}usser, Michael and Turaga, Srinivas C}, editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett} } @conference {47736, title = {Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems.}, booktitle = { IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro}, year = {2016}, month = {2016 Jun 15}, address = {Clarion Congress Hotel in Prague Prague; Czech Republic}, abstract = {

With recent advances in high-throughput Electron Microscopy (EM) imaging it is now possible to image an entire nervous system of organisms like Drosophila melanogaster. One of the bottlenecks to reconstruct a connectome from these large volumes ({\oe} 100 TiB) is the pixel-wise prediction of membranes. The time it would typically take to process such a volume using a convolutional neural network (CNN) with a sliding window approach is in the order of years on a current GPU. With sliding windows, however, a lot of redundant computations are carried out. In this paper, we present an extension to the Caffe library to increase throughput by predicting many pixels at once. On a sliding window network successfully used for membrane classification, we show that our method achieves a speedup of up to 57{\texttimes}, maintaining identical prediction results.

}, isbn = {978-147992350-2}, doi = {10.1109/ISBI.2016.7493487}, author = {Tschopp, F and Martel, J N P and Turaga, Srinivas C and Cook, M and Funke, Jan} } @article {47159, title = {Crowdsourcing the creation of image segmentation algorithms for connectomics.}, journal = {Frontiers in Neuroanatomy}, volume = {9}, year = {2015}, month = {2015 Nov 05}, pages = {142}, abstract = {

To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This {\textquotedblleft}deep learning{\textquotedblright} approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.

}, doi = {10.3389/fnana.2015.00142}, author = {Arganda-Carreras, Ignacio and Turaga, Srinivas C and Berger, Daniel R and Ciresan, Dan and Giusti, Alessandro and Gambardella, Luca M and Schmidhuber, Juergen and Laptev, Dmitry and Dwivedi, Sarvesh and Buhmann, Joachim M} } @article {46326, title = {Mapping social behavior-induced brain activation at cellular resolution in the mouse.}, journal = {Cell Reports}, volume = {10}, year = {2015}, month = {2015 Jan 13}, pages = {292-305}, abstract = {

Understanding how brain activation mediates behaviors is a central goal of systems neuroscience. Here, we apply an automated method for mapping brain activation in the mouse in order to probe how sex-specific social behaviors are represented in the male brain. Our method uses the immediate-early-gene c-fos, a marker of neuronal activation, visualized by serial two-photon tomography: the c-fos-GFP+ neurons are computationally detected, their distribution is registered to a reference brain and a brain atlas, and their numbers are analyzed by statistical tests. Our results reveal distinct and shared female and male interaction-evoked patterns of male brain activation representing sex discrimination and social recognition. We also identify brain regions whose degree of activity correlates to specific features of social behaviors and estimate the total numbers and the densities of activated neurons per brain areas. Our study opens the door to automated screening of behavior-evoked brain activation in the mouse.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2014.12.014}, author = {Kim, Yongsoo and Venkataraju, Kannan Umadevi and Pradhan, Kith and Mende, Carolin and Taranda, Julian and Turaga, Srinivas C and Arganda-Carreras, Ignacio and Ng, Lydia and Hawrylycz, Michael J and Rockland, Kathleen S and Seung, H Sebastian and Osten, Pavel} } @article {46327, title = {Space-time wiring specificity supports direction selectivity in the retina.}, journal = {Nature}, volume = {509}, year = {2014}, month = {2014 May 15}, pages = {331-6}, abstract = {

How does the mammalian retina detect motion? This classic problem in visual neuroscience has remained unsolved for 50 years. In search of clues, here we reconstruct Off-type starburst amacrine cells (SACs) and bipolar cells (BCs) in serial electron microscopic images with help from EyeWire, an online community of {\textquoteright}citizen neuroscientists{\textquoteright}. On the basis of quantitative analyses of contact area and branch depth in the retina, we find evidence that one BC type prefers to wire with a SAC dendrite near the SAC soma, whereas another BC type prefers to wire far from the soma. The near type is known to lag the far type in time of visual response. A mathematical model shows how such {\textquoteright}space-time wiring specificity{\textquoteright} could endow SAC dendrites with receptive fields that are oriented in space-time and therefore respond selectively to stimuli that move in the outward direction from the soma.

}, keywords = {Amacrine Cells, Animals, Artificial Intelligence, Brain Mapping, Crowdsourcing, Dendrites, Mice, Models, Neurological, Motion, Neural Pathways, Presynaptic Terminals, Retina, Retinal Bipolar Cells, Spatio-Temporal Analysis}, issn = {1476-4687}, doi = {10.1038/nature13240}, author = {Kim, Jinseop S and Greene, Matthew J and Zlateski, Aleksandar and Lee, Kisuk and Richardson, Mark and Turaga, Srinivas C and Purcaro, Michael and Balkam, Matthew and Robinson, Amy and Behabadi, Bardia F and Campos, Michael and Denk, Winfried and Seung, H Sebastian} } @article {6261, title = {Connectomic reconstruction of the inner plexiform layer in the mouse retina.}, journal = {Nature}, volume = {500}, year = {2013}, month = {2013 Aug 8}, pages = {168-74}, abstract = {

Comprehensive high-resolution structural maps are central to functional exploration and understanding in biology. For the nervous system, in which high resolution and large spatial extent are both needed, such maps are scarce as they challenge data acquisition and analysis capabilities. Here we present for the mouse inner plexiform layer{\textendash}the main computational neuropil region in the mammalian retina{\textendash}the dense reconstruction of 950 neurons and their mutual contacts. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. We characterize a new type of retinal bipolar interneuron and show that we can subdivide a known type based on connectivity. Circuit motifs that emerge from our data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive.

}, keywords = {Amacrine Cells, Animals, Biological, Cell Communication, Computer-Assisted, Connectome, Electron, Image Processing, Inbred C57BL, Mice, Microscopy, Models, Neuropil, Retina, Retinal Ganglion Cells}, issn = {1476-4687}, doi = {10.1038/nature12346}, author = {Helmstaedter, Moritz and Briggman, Kevin L and Turaga, Srinivas C and Jain, Viren and Seung, H Sebastian and Denk, Winfried} } @article {46575, title = {Inferring neural population dynamics from multiple partial recordings of the same neural circuit.}, journal = {Advances in Neural Information Processing Systems (NIPS) }, volume = {26}, year = {2013}, month = {2013}, abstract = {

Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed. It is now possible to measure the activity of hundreds of neurons using 2-photon calcium imaging. However, many computations are thought to involve circuits consisting of thousands of neurons, such as cortical barrels in rodent somatosensory cortex. Here we contribute a statistical method for stitching" together sequentially imaged sets of neurons into one model by phrasing the problem as fitting a latent dynamical system with missing observations. This method allows us to substantially expand the population-sizes for which population dynamics can be characterized---beyond the number of simultaneously imaged neurons. In particular, we demonstrate using recordings in mouse somatosensory cortex that this method makes it possible to predict noise correlations between non-simultaneously recorded neuron pairs.

}, url = {http://papers.nips.cc/paper/4874-inferring-neural-population-dynamics-from-multiple-partial-recordings-of-the-same-neural-circuit}, author = {Turaga, Srinivas C and Buesing, Lars and Packer, Adam M. and Dalgleish, Henry and Pettit, Noah and H{\"a}usser, Michael and Macke, Jakob H.} } @article {3844, title = {Convolutional networks can learn to generate affinity graphs for image segmentation.}, journal = {Neural Computation}, volume = {22}, year = {2010}, month = {2010 Feb}, pages = {511-38}, abstract = {

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Electron, Image Processing, Mathematical Computing, Mathematical Concepts, Mathematics, Microscopy, Neural Networks (Computer), Pattern Recognition}, issn = {1530-888X}, doi = {10.1162/neco.2009.10-08-881}, author = {Turaga, Srinivas C and Murray, Joseph F and Jain, Viren and Roth, Fabian and Helmstaedter, Moritz and Briggman, Kevin and Denk, Winfried and Seung, Hyunjune Sebastian} } @article {3843, title = {Machines that learn to segment images: a crucial technology for connectomics.}, journal = {Current Opinion in Neurobiology}, volume = {20}, year = {2010}, month = {2010 Oct}, pages = {653-66}, abstract = {

Connections between neurons can be found by checking whether synapses exist at points of contact, which in turn are determined by neural shapes. Finding these shapes is a special case of image segmentation, which is laborious for humans and would ideally be performed by computers. New metrics properly quantify the performance of a computer algorithm using its disagreement with {\textquoteright}true{\textquoteright} segmentations of example images. New machine learning methods search for segmentation algorithms that minimize such metrics. These advances have reduced computer errors dramatically. It should now be faster for a human to correct the remaining errors than to segment an image manually. Further reductions in human effort are expected, and crucial for finding connectomes more complex than that of Caenorhabditis elegans.

}, keywords = {Algorithms, Animals, Artificial Intelligence, Computer-Assisted, Electron, Humans, Image Processing, Microscopy, Nanotechnology, Neurobiology}, issn = {1873-6882}, doi = {10.1016/j.conb.2010.07.004}, author = {Jain, Viren and Seung, Hyunjune Sebastian and Turaga, Srinivas C} }