Real-Time Neuron Detection and Neural Signal Extraction Platform for Miniature Calcium Imaging

Lee, Yaesop; Xie, Jing; Lee, Eung Joo; Sudarsanan, Srijesh; Lin, Da-Ting; Chen, Rong; Bhattacharyya, Shuvra S.

doi:10.3389/fncom.2020.00043

Cited by 8 publications

(15 citation statements)

References 40 publications

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“…The overall time complexity per iteration of NEDECO using PSO optimization is P × F where F denotes the fitness evaluation time complexity when PNDS runs one time on the training dataset. No complexity expression for F is provided in PNDS, which are NDSEP (Lee et al, 2020) and CellSort (Mukamel et al, 2009) in our experiments, so it's difficult to derive because of third party functions involved.…”

Section: Mode Input Outputmentioning

confidence: 99%

“…The parameter settings resulting from this hand-optimization process are discussed in Minimum, maximum, quartiles and average of the ODC over all k = experimental groups for NEDECO-CellSort. Lee et al (2020). NEDECO-CellSort eliminates the need for such labor-intensive hand-optimization, and at the same time, produces significantly more accurate and efficient configurations of CellSort.…”

Section: Cellsortmentioning

confidence: 99%

“…By “generalized”, we mean that the tool works comprehensively over all decoding algorithm parameters, works across a wide variety of model types and associated information extraction algorithms rather than being restricted to a specific type of model, and takes into account both the neural decoding accuracy and run-time efficiency. In Section 4, we demonstrate the flexibility of NEDECO by applying it to two significantly different neural decoding tools from the literature — the Neuron Detection and Signal Extraction Platform (NDSEP) (Lee et al, 2020 ), and CellSort (Mukamel et al, 2009 ), using two different methods as the underlying search strategy — particle swarm optimization and genetic algorithms. Our experiments demonstrate the ability of NEDECO to derive parameter settings that lead to significantly improved neural decoding performance compared to the previous published results using NDSEP and CellSort, which are based on hand-tuned parameters.…”

Section: Background and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A parameter-optimization framework for neural decoding systems

Xie

Chen²,

Bhattacharyya³

2023

Front. Neuroinform.

Self Cite

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Real-time neuron detection and neural activity extraction are critical components of real-time neural decoding. They are modeled effectively in dataflow graphs. However, these graphs and the components within them in general have many parameters, including hyper-parameters associated with machine learning sub-systems. The dataflow graph parameters induce a complex design space, where alternative configurations (design points) provide different trade-offs involving key operational metrics including accuracy and time-efficiency. In this paper, we propose a novel optimization framework that automatically configures the parameters in different neural decoders. The proposed optimization framework is evaluated in depth through two case studies. Significant performance improvement in terms of accuracy and efficiency is observed in both case studies compared to the manual parameter optimization that was associated with the published results of those case studies. Additionally, we investigate the application of efficient multi-threading strategies to speed-up the running time of our parameter optimization framework. Our proposed optimization framework enables efficient and effective estimation of parameters, which leads to more powerful neural decoding capabilities and allows researchers to experiment more easily with alternative decoding models.

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Section: Mode Input Outputmentioning

confidence: 99%

Section: Cellsortmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A parameter-optimization framework for neural decoding systems

Xie

Chen²,

Bhattacharyya³

2023

Front. Neuroinform.

Self Cite

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“…Programs that process in realtime have gradually become commonly used. Lee et al implemented a system based on a data-flow framework that enables the replacement of modules [20]. Since their module for spatial component extraction was based on SimpleBlob-Detector in OpenCV, it is not effective enough for unclear images obtained from a 1p miniscope.…”

Section: B Image Processing Of Calcium Imaging Datamentioning

confidence: 99%

Open-Source Software for Real-time Calcium Imaging and Synchronized Neuron Firing Detection

Takeuchi

Tezuka

Vergara

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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We developed Carignan, a real-time calcium imaging software that can automatically detect activity patterns of neurons. Carignan can activate an external device when synchronized neural activity is detected in calcium imaging obtained by a one-photon (1p) miniscope. Combined with optogenetics, our software enables closed-loop experiments for investigating functions of specific types of neurons in the brain. In addition to making existing pattern detection algorithms run in real-time seamlessly, we developed a new classification module that distinguishes neurons from false-positives using deep learning. We used a combination of convolutional and recurrent neural networks to incorporate both spatial and temporal features in activity patterns. Our method performed better than existing neuron detection methods for false-positive neuron detection in terms of the F1 score. Using Carignan, experimenters can activate or suppress a group of neurons when specific neural activity is observed. Because the system uses a 1p miniscope, it can be used on the brain of a freely-moving animal, making it applicable to a wide range of experimental paradigms.

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“…Here, two-photon microscopy is used to capture images of a neuronal population that expresses a fluorescent calcium indicator and allows to visualize the increase in intracellular Ca2+concentration accompanying neurons' spiking activity [5]. More generally, 'neural decoding' refers to techniques that use brain signals to make predictions about behaviour, perception, or cognitive state and are becoming increasingly important for neuroscientific research [6][7][8][9]. For example, calcium imaging techniques enable optical measurement of large neural populations at a high spatio-temporal resolution and thus facilitate insights into neural activity [5,10,11].…”

Section: Introductionmentioning

confidence: 99%

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

Eichin

Hackenberg

Broichhagen

et al. 2021

Preprint

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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.

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Real-Time Neuron Detection and Neural Signal Extraction Platform for Miniature Calcium Imaging

Cited by 8 publications

References 40 publications

A parameter-optimization framework for neural decoding systems

A parameter-optimization framework for neural decoding systems

Open-Source Software for Real-time Calcium Imaging and Synchronized Neuron Firing Detection

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

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