Neuro-choice
Extracting Neural Activity Signals from Large-scale Calcium Imaging Data
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Benjamín Béjar received a PhD in Electrical Engineering from Universidad Politécnica de Madrid in 2012. He served as a postdoctoral fellow at École Polytechnique Fédérale de Lausanne until 2017, and then he moved to Johns Hopkins University where he held a Research Faculty position until Dec. 2019. His research interests lie at the intersection of signal processing and machine learning methods, and he has worked on topics such as sparse signal recovery, time-series analysis, and computer vision methods with special emphasis on biomedical applications. Since 2021, Benjamin leads the SDSC office at the Paul Scherrer Institute in Villigen.
Guillaume Obozinski graduated with a PhD in Statistics from UC Berkeley in 2009. He did his postdoc and held until 2012 a researcher position in the Willow and Sierra teams at INRIA and Ecole Normale Supérieure in Paris. He was then Research Faculty at Ecole des Ponts ParisTech until 2018. Guillaume has broad interests in statistics and machine learning and worked over time on sparse modeling, optimization for large scale learning, graphical models, relational learning and semantic embeddings, with applications in various domains from computational biology to computer vision.
Izabela holds a PhD degree in Computer Science from University of Rennes 1, France and the National French Institute for Research in Computer Science and Automatics (INRIA), France. Before joining the SDSC, she was a postdoctoral researcher at the Chair of Computational Social Science at ETH Zurich and a lecturer for the “Data Science in Techno-Socio-Economic Systems” course at ETH Zurich. Her main research focus is on big data analytics, tools and platforms, machine learning and data mining, large scale network analysis, in the particular setting of social data mining.
description
Problem:
The partners in the project use high throughput optical fluorescence microscopy and calcium imaging to record and track the activity of large, genetically identified neuronal cell populations in freely moving mice over long periods of time (several months). By analyzing these large-scale high-resolution images, our goals are to:
- develop a fully automated image classification algorithm that extracts all neuron outlines, positions and activities
- scale and parallelize classifier training to achieve the best performance
- benchmark the new classifier against human labeling
Solution:
Most state-of-the-art methods for extracting neuronal activity from calcium imaging data are semi-automated or require full supervision from a human expert, making it very difficult to scale to large datasets.
Our solution (so far) relies on (convolutional) dictionary learning models. Dictionary learning and sparse representation make a sparsity assumption instead of independence or uncorrelation (like PCA or ICA), which is more aligned with the sparseness of neuronal activity property.
We propose a structured dictionary learning model that introduces sparse activations of neurons, temporally continuous activations and spatial smoothness in the masks modelling the neurons illumination patterns. The algorithm uses block proximal gradient methods for learning the dictionary elements and activation matrix.
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