239 citations · 428 across the 13 of their papers we have counts for
14 papers
Integrative neurocybernetic modeling in the era of large-scale neuroscience
Il Memming Park, Ayesha Vermani, Gonzalo G. de Polavieja +12
Large-scale neuroscience is generating rich datasets across animals, brain areas, and behavioral contexts, yet our modeling efforts remain fragmented across isolated experiments. W…
FourierNets enable the design of highly non-local optical encoders for computational imaging
Diptodip Deb, Zhenfei Jiao, Ruth Sims +5
Differentiable simulations of optical systems can be combined with deep learning-based reconstruction networks to enable high performance computational imaging via end-to-end (E2E)…
Learning Guided Electron Microscopy with Active Acquisition
Lu Mi, Hao Wang, Yaron Meirovitch +5
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection. Datasets are typic…
Teaching deep neural networks to localize single molecules for super-resolution microscopy
Artur Speiser, Lucas-Raphael Müller, Ulf Matti +5
Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Acc…
Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data
Daniel Jiwoong Im, Sridhama Prakhya, Jinyao Yan +2
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,…
Synaptic partner prediction from point annotations in insect brains
Julia Buhmann, Renate Krause, Rodrigo Ceballos Lentini +4
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. C…
A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System
Fabian David Tschopp, Michael B. Reiser, Srinivas C. Turaga
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 con…
Discrete flow posteriors for variational inference in discrete dynamical systems
Laurence Aitchison, Vincent Adam, Srinivas C. Turaga
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…
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H. Macke
A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Curre…
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Artur Speiser, Jinyao Yan, Evan Archer +3
Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are nec…
Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction
Jan Funke, Fabian David Tschopp, William Grisaitis +4
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…
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Toufiq Parag, Fabian Tschopp, William Grisaitis +6
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…
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Furong Huang, Animashree Anandkumar, Christian Borgs +6
Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing c…
Maximin affinity learning of image segmentation
Srinivas C. Turaga, Kevin L. Briggman, Moritz Helmstaedter +2
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 g…