@article {68656, title = {Connectome-constrained networks predict neural activity across the fly visual system}, journal = {Nature}, year = {2024}, month = {2024 Sep 11}, abstract = {

We can now measure the connectivity of every neuron in a neural circuit, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single-neuron and single-synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion. Our mechanistic model makes detailed, experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 26 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected-a universally observed feature of biological neural networks across species and brain regions.

}, doi = {10.1038/s41586-024-07939-3}, url = {https://doi.org/10.1038/s41586-024-07939-3}, author = {Janne K. Lappalainen and Fabian D. Tschopp and Sridhama Prakhya and Mason McGill and Aljoscha Nern and Kazunori Shinomiya and Shin-ya Takemura and Eyal Gruntman and Jakob H. Macke and Srinivas C. Turaga} } @booklet {67224, title = {Teaching deep neural networks to localize single molecules for super-resolution microscopy}, year = {2020}, abstract = {

Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.

}, url = {https://arxiv.org/abs/1907.00770}, author = {Artur Speiser and Lucas-Raphael M{\"u}ller and Ulf Matti and Christopher J. Obara and Wesley R. Legant and Jonas Ries and Jakob H. Macke and Srinivas C. Turaga} } @article {67223, title = {Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data}, journal = {CoRR}, volume = {abs/1906.03214}, year = {2019}, month = {10/2019}, abstract = {

The Importance Weighted Auto Encoder (IWAE) objective has been shown to improve the training of generative models over the standard Variational Auto Encoder (VAE) objective. Here, we derive importance weighted extensions to Adversarial Variational Bayes (AVB) and Adversarial Autoencoder (AAE). These latent variable models use implicitly defined inference networks whose approximate posterior density qφ(z|x) cannot be directly evaluated, an essential ingredient for importance weighting. We show improved training and inference in latent variable models with our adversarially trained importance weighting method, and derive new theoretical connections between adversarial generative model training criteria and marginal likelihood based methods. We apply these methods to the important problem of inferring spiking neural activity from calcium imaging data, a challenging posterior inference problem in neuroscience, and show that posterior samples from the adversarial methods outperform factorized posteriors used in VAEs.

}, url = {http://arxiv.org/abs/1906.03214}, author = {Daniel Jiwoong Im and Sridhama Prakhya and Jinyao Yan and Srinivas C. Turaga and Kristin Branson} } @article {67220, title = {Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration}, journal = {CoRR}, volume = {abs/1707.08935}, year = {2017}, abstract = {

The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic (e.g., SBEM) data. In spite of the remarkable progress on algorithms in recent years, automatic dense reconstruction from anisotropic data remains a challenge for the connectomics community. One significant hurdle in the segmentation of anisotropic data is the difficulty in generating a suitable initial over-segmentation. In this study, we present a segmentation method for anisotropic EM data that agglomerates a 3D over-segmentation computed from the 3D affinity prediction. A 3D U-net is trained to predict 3D affinities by the MALIS approach. Experiments on multiple datasets demonstrates the strength and robustness of the proposed method for anisotropic EM segmentation.

}, url = {https://arxiv.org/abs/1707.08935v2}, author = {Toufiq Parag and Fabian Tschopp and William Grisaitis and Srinivas C. Turaga and Xuewen Zhang and Brian Matejek and Lee Kamentsky and Jeff W. Lichtman and Hanspeter Pfister} } @proceedings {46351, title = {Learning to Agglomerate Superpixel Hierarchies}, volume = {24}, year = {2011}, month = {12/2011}, publisher = {MIT Press}, address = {Granada, Spain}, abstract = {

An agglomerative clustering algorithm merges the most similar pair of clusters at every iteration. The function that evaluates similarity is traditionally hand- designed, but there has been recent interest in supervised or semisupervised settings in which ground-truth clustered data is available for training. Here we show how to train a similarity function by regarding it as the action-value function of a reinforcement learning problem. We apply this general method to segment images by clustering superpixels, an application that we call Learning to Agglomerate Superpixel Hierarchies (LASH). When applied to a challenging dataset of brain images from serial electron microscopy, LASH dramatically improved segmentation accuracy when clustering supervoxels generated by state of the boundary detection algorithms. The naive strategy of directly training only supervoxel similarities and applying single linkage clustering produced less improvement.

}, author = {Viren Jain and Srinivas C. Turaga and K Briggman and Moritz N. Helmstaedter and Winfried Denk and H. S. Seung} } @proceedings {46329, title = {Maximin affinity learning of image segmentation}, volume = {22}, year = {2009}, publisher = {MIT Press}, abstract = {

Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are good in the sense of minimizing edge misclassification rates. However, this error measure is only indirectly related to the quality of segmentations produced by ultimately partitioning the affinity graph. We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well known segmentation performance measure. The Rand index measures segmentation performance by quantifying the classification of the connectivity of image pixel pairs after segmentation. By using the simple graph partitioning algorithm of finding the connected components of the thresholded affinity graph, we are able to train an affinity classifier to directly minimize the Rand index of segmentations resulting from the graph partitioning. Our learning algorithm corresponds to the learning of maximin affinities between image pixel pairs, which are predictive of the pixel-pair connectivity.

}, author = {Srinivas C. Turaga and Kevin Briggman and Moritz N. Helmstaedter and Winfried Denk and Sebastian Seung} } @article {46352, title = {Calculating free energies for diffusion in tight-fitting zeolite-guest systems: Local normal-mode Monte Carlo}, journal = {Journal of Chemical Physics}, volume = {118}, year = {2003}, month = {2003}, type = {Journal Article}, abstract = {

We present an efficient Monte Carlo algorithm for simulating diffusion in tight-fitting host{\textendash}guest systems, based on using zeolitenormal modes. Computational efficiency is gained by sampling framework distortions using normal-mode coordinates, and by exploiting the fact that zeolite distortion energies are well approximated by harmonic estimates. Additional savings are obtained by performing local normal-mode analysis, i.e., only including the motions of zeolite atoms close to the jumping molecule, hence focusing the calculation on zeolite distortions relevant to guest diffusion. We performed normal-mode analysis on various silicalite structures to demonstrate the accuracy of the harmonic approximation. We computed free energy surfaces for benzene in silicalite, finding excellent agreement with previous theoretical studies. Our method is found to be orders-of-magnitude faster than comparable Monte Carlo calculations that use conventional forcefields to quantify zeolite distortion energies. For tight-fitting guests, the efficiency of our new method allows flexible-lattice simulations to converge in less CPU time than that required for fixed-lattice simulations, because of the increased likelihood of jumping through a flexible lattice.

}, issn = {0021-9606}, doi = {10.1063/1.1558033}, author = {Srinivas C. Turaga and Scott M. Auerbach} }