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27 April 2020 Multichannel spike deconvolution in calcium imaging (Conference Presentation)
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Abstract
The problem of accurately inferring the firing time of neurons from fluorescent microscopy measurements such as calcium imaging is crucial for deciphering the dynamics of neural spiking activity during different tasks. The fluorescence signals from calcium images can be modeled as the convolution of the neural spikes (action potential) with an exponentially decaying kernel whose decay is governed by the type of calcium indicator used. Calcium signals exhibit excellent spatial resolution and it is possible to record individual neural activity from a large population of neurons. However, the main drawback of calcium imaging is that it has a poor temporal resolution due to the slow dynamics of calcium indicators and scanning limitations of existing microscopes. Existing spike deconvolution algorithms obtain a representation of spiking activity at a rate that is identical to the acquisition rate of calcium signals (typically <60Hz). However, this does not accurately capture the true spiking activity as typical neural spike separation could be <<5ms. In this paper, we show that simultaneously using the measurements from multiple neurons can be combined with accurate modeling of spiking activity to overcome these limitations. The main idea is to utilize the inherent multichannel structure of the problem. Calcium traces from different neurons will be considered as the output of the same unknown filter excited by different inputs corresponding to the spiking activity of different neurons. We will develop a sparse reconstruction algorithm that can solve this multichannel blind deconvolution problem from subsampled measurements and simultaneously recover the sparse neural activity at a rate that is representative of the true neural activity.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pulak Sarangi and Piya Pal "Multichannel spike deconvolution in calcium imaging (Conference Presentation)", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 113950E (27 April 2020); https://doi.org/10.1117/12.2558792
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