scholarly journals Bayesian hypothesis testing and experimental design for two-photon imaging data

2019 ◽  
Vol 15 (8) ◽  
pp. e1007205 ◽  
Author(s):  
Luke E. Rogerson ◽  
Zhijian Zhao ◽  
Katrin Franke ◽  
Thomas Euler ◽  
Philipp Berens
Keyword(s):  
Experimental Design ◽  
Hypothesis Testing ◽  
Imaging Data ◽  
Two Photon ◽  
Bayesian Hypothesis Testing ◽  
Photon Imaging ◽  
Two Photon Imaging

scholarly journals Correction: Bayesian hypothesis testing and experimental design for two-photon imaging data

2019 ◽  
Vol 15 (10) ◽  
pp. e1007473
Author(s):  
Luke E. Rogerson ◽  
Zhijian Zhao ◽  
Katrin Franke ◽  
Thomas Euler ◽  
Philipp Berens
Keyword(s):  
Experimental Design ◽  
Hypothesis Testing ◽  
Imaging Data ◽  
Two Photon ◽  
Bayesian Hypothesis Testing ◽  
Photon Imaging ◽  
Two Photon Imaging

scholarly journals Incorporating structural knowledge into unsupervised deep learning for two-photon imaging data

2021 ◽  
Author(s):  
Florian Eichin ◽  
Maren Hackenberg ◽  
Caroline Broichhagen ◽  
Antje Kilias ◽  
Jan Schmoranzer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Live Imaging ◽  
Temporal Changes ◽  
Generative Models ◽  
Structural Knowledge ◽  
Imaging Data ◽  
Two Photon ◽  
Latent Space ◽  
Photon Imaging ◽  
Two Photon Imaging

Live imaging techniques, such as two-photon imaging, promise novel insights into cellular activity patterns at a high spatial and temporal resolution. While current deep learning approaches typically focus on specific supervised tasks in the analysis of such data, e.g., learning a segmentation mask as a basis for subsequent signal extraction steps, we investigate how unsupervised generative deep learning can be adapted to obtain interpretable models directly at the level of the video frames. Specifically, we consider variational autoencoders for models that infer a compressed representation of the data in a low-dimensional latent space, allowing for insight into what has been learned. Based on this approach, we illustrate how structural knowledge can be incorporated into the model architecture to improve model fitting and interpretability. Besides standard convolutional neural network components, we propose an architecture for separately encoding the foreground and background of live imaging data. We exemplify the proposed approach with two-photon imaging data from hippocampal CA1 neurons in mice, where we can disentangle the neural activity of interest from the neuropil background signal. Subsequently, we illustrate how to impose smoothness constraints onto the latent space for leveraging knowledge about gradual temporal changes. As a starting point for adaptation to similar live imaging applications, we provide a Jupyter notebook with code for exploration. Taken together, our results illustrate how architecture choices for deep generative models, such as for spatial structure, foreground vs. background, and gradual temporal changes, facilitate a modeling approach that combines the flexibility of deep learning with the benefits of incorporating domain knowledge. Such a strategy is seen to enable interpretable, purely image-based models of activity signals from live imaging, such as for two-photon data.

scholarly journals Multiscale vision model for event detection and reconstruction in two-photon imaging data

2014 ◽  
Vol 1 (1) ◽  
pp. 011012 ◽  
Author(s):  
Alexey Brazhe ◽  
Claus Mathiesen ◽  
Barbara Lind ◽  
Andrey Rubin ◽  
Martin Lauritzen
Keyword(s):  
Event Detection ◽  
Imaging Data ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging

scholarly journals Active learning of cortical connectivity from two-photon imaging data

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0196527 ◽  
Author(s):  
Martín A. Bertrán ◽  
Natalia L. Martínez ◽  
Ye Wang ◽  
David Dunson ◽  
Guillermo Sapiro ◽  
...  
Keyword(s):  
Active Learning ◽  
Imaging Data ◽  
Cortical Connectivity ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging

scholarly journals Active Learning of Cortical Connectivity from Two-Photon Imaging Data

2018 ◽  
Author(s):  
Martín Bertrán ◽  
Natalia Martínez ◽  
Ye Wang ◽  
David Dunson ◽  
Guillermo Sapiro ◽  
...  
Keyword(s):  
Active Learning ◽  
Small World ◽  
Real Data ◽  
Sensory Stimulation ◽  
Imaging Data ◽  
Population Activity ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging

AbstractUnderstanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network.. Unlike existing system identification techniques, this “active learning” method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

scholarly journals Bayesian Hypothesis Testing And Experimental Design For Two-Photon Imaging Data

2018 ◽  
Author(s):  
Luke E. Rogerson ◽  
Zhijian Zhao ◽  
Katrin Franke ◽  
Philipp Berens ◽  
Thomas Euler
Keyword(s):  
Neural Activity ◽  
Ex Vivo ◽  
Gaussian Process Regression ◽  
Mouse Retina ◽  
Central Feature ◽  
Imaging Data ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging ◽  
The Cost

AbstractVariability, stochastic or otherwise, is a central feature of neural circuits. Yet the means by which variation and uncertainty are derived from noisy observations of neural activity is often unprincipled, with too much weight placed on numerical convenience at the cost of statistical rigour. For two-photon imaging data, composed of fundamentally probabilistic streams of photon detections, the problem is particularly acute. Here, we present a complete statistical pipeline for the inference and analysis of neural activity using Gaussian Process Regression, applied to two-photon recordings of light-driven activity in ex vivo mouse retina. We demonstrate the flexibility and extensibility of these models, considering cases with non-stationary statistics, driven by complex parametric stimuli, in signal discrimination, hierarchical clustering and inference tasks. Sparse approximation methods allow these models to be fitted rapidly, permitting them to actively guiding the design of light stimulation in the midst of ongoing two-photon experiments.

scholarly journals Interpreting two-photon imaging data of lymphocyte motility

2005 ◽  
Vol 71 (6) ◽  
Author(s):  
Michael E. Meyer-Hermann ◽  
Philip K. Maini
Keyword(s):  
Imaging Data ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging ◽  
Lymphocyte Motility
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