@article {69471, title = {Norepinephrine changes behavioral state via astroglial purinergic signaling}, journal = {Science}, year = {2024}, month = {2025 May 15}, abstract = {

Both neurons and glia communicate through diffusible neuromodulators; however, how neuron-glial interactions in such neuromodulatory networks influence circuit computation and behavior is unclear. During futility-induced behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine (NE) drives fast excitation and delayed inhibition of behavior and circuit activity. We found that astroglial purinergic signaling implements the inhibitory arm of this motif. In larval zebrafish, NE triggers astroglial release of adenosine triphosphate (ATP), extracellular conversion of ATP into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. Our results suggest a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in NE-mediated behavioral and brain state transitions and position astroglia as important effectors in neuromodulatory signaling.

Preprint:\ https://www.biorxiv.org/content/early/2024/05/23/2024.05.23.595576

}, doi = {10.1126/science.adq5233}, url = {https://www.science.org/stoken/author-tokens/ST-2654/full}, author = {Chen, Alex B. and Duque, Marc and Wang, Vickie M. and Dhanasekar, Mahalakshmi and Mi, Xuelong and Rymbek, Altyn and Tocquer, Loeva and Narayan, Sujatha and Prober, David and Yu, Guoqiang and Wyart, Claire and Engert, Florian and Ahrens, Misha B.} } @booklet {67972, title = {Programmable 3D snapshot microscopy with Fourier convolutional networks}, year = {2021}, abstract = {

3D snapshot microscopy enables fast volumetric imaging by capturing a 3D volume in a single 2D camera image and performing computational reconstruction. Fast volumetric imaging has a variety of biological applications such as whole brain imaging of rapid neural activity in larval zebrafish. The optimal microscope design for this optical 3D-to-2D encoding is both sample- and task-dependent, with no general solution known. Deep learning based decoders can be combined with a differentiable simulation of an optical encoder for end-to-end optimization of both the deep learning decoder and optical encoder. This technique has been used to engineer local optical encoders for other problems such as depth estimation, 3D particle localization, and lensless photography. However, 3D snapshot microscopy is known to require a highly non-local optical encoder which existing UNet-based decoders are not able to engineer. We show that a neural network architecture based on global kernel Fourier convolutional neural networks can efficiently decode information from multiple depths in a volume, globally encoded across a 3D snapshot image. We show in simulation that our proposed networks succeed in engineering and reconstructing optical encoders for 3D snapshot microscopy where the existing state-of-the-art UNet architecture fails. We also show that our networks outperform the state-of-the-art learned reconstruction algorithms for a computational photography dataset collected on a prototype lensless camera which also uses a highly non-local optical encoding.

}, keywords = {Computer Vision and Pattern Recognition (cs.CV), electronic engineering, FOS: Computer and information sciences, FOS: Electrical engineering, Image and Video Processing (eess.IV), information engineering, Machine Learning (cs.LG)}, doi = {10.48550/ARXIV.2104.10611}, url = {https://arxiv.org/abs/2104.10611}, author = {Deb, Diptodip and Jiao, Zhenfei and Chen, Alex B. and Broxton, Michael and Ahrens, Misha B. and Podgorski, Kaspar and Turaga, Srinivas C.} }