AI in EE

AI IN DIVISIONS

AI in Signal Division

AI in EE

AI IN DIVISIONS

AI in Signal Division ​ ​

AI in Signal Division

3DM: deep decomposition and deconvolution microscopy for rapid neural activity imaging

Title : 3DM: deep decomposition and deconvolution microscopy for rapid neural activity imaging

Author : Eun-Seo Cho*, Seungjae Han*, Kang-Han Lee, Cheol-Hee Kim, Young-Gyu Yoon (* co-first authors)

Journal/Conference and Year : Optics Express, 2021

Abstract : We report the development of deep decomposition and deconvolution microscopy (3DM), a computational microscopy method for the volumetric imaging of neural activity. 3DM overcomes the major challenge of deconvolution microscopy, the ill-posed inverse problem. We take advantage of the temporal sparsity of neural activity to reformulate and solve the inverse problem using two neural networks which perform sparse decomposition and deconvolution. We demonstrate the capability of 3DM via in vivo imaging of the neural activity of a whole larval zebrafish brain with a field of view of 1040µm×400µm×235µm and with estimated lateral and axial resolutions of 1.7µm and 5.4µm, respectively, at imaging rates of up to 4.2 volumes per second.